Loading libraries (1)

#Loading the EDA libraries
library(tools)
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.5.3
library(tidyverse)
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library(Hmisc)
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## Warning: package 'survival' was built under R version 3.5.3
library(xlsx)
library(ggplot2)
library(gridExtra)
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library(reshape2)
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library(tidyr)
library(psych)
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library(caret)
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library(readr)
library(car)
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library(purrr)
library(data.table)
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library(fastDummies)
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#Loading data imputation libraries
library(mice)
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library(VIM)
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library(outliers)
library(DMwR)
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#Loading the modelling libraries
library(e1071)
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library(ipred)
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library(RWeka)
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library(rpart)
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library(rpart.plot)
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library(InformationValue)
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library(class)
library(MLmetrics)
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library(randomForest)
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library(party)
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library(dlookr)
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library(rpart)
library(rattle)
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library(DescTools)
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library(lmtest)
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library(xgboost)
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library(Matrix)
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library(DiagrammeR)
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library(partykit)
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library(MLeval)
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library(doSNOW)
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library(parallel)

File loading and basic exploration(2)

fName <- "C:/BigData/BABI/Capstone/Coronory Heart Risk Study/Coronary_heart_risk_study.csv"

loaddata <- function(fileName,sheetName1)
{
  if (file_ext(fName) == "csv")
  {
    data <- read.csv(fName, header = TRUE,na.strings=c("", "NA"))
  }
  else if (file_ext(fName) == "txt")
  {
    data <- read.csv2(fName)
  }
  else if (file_ext(fName) == "xlsx")
  {
    data <- read.xlsx(fName,sheetName = sheetName1,as.data.frame = TRUE, header = TRUE)
  }
  return (data)
}

data <- loaddata(fName,"")

data<-dplyr::rename(data,"prevStroke"="prevalentStroke")
data<-dplyr::rename(data,"prevHyp"="prevalentHyp")
data<-dplyr::rename(data,"curSmoker"="currentSmoker")


data %>%
  summarise_all(funs(sum(is.na(.)))) %>%
  gather %>%
  ggplot(aes(x = reorder(key, value), y = value)) + geom_bar(stat = "identity",fill="steelblue",color="steelblue") +
  coord_flip() +
  xlab("Variables") +
  ylab("Initial absolute number of missings")
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.

data %>% 
  select_if(is.numeric) %>% names
##  [1] "male"       "age"        "education"  "curSmoker"  "cigsPerDay"
##  [6] "BPMeds"     "prevStroke" "prevHyp"    "diabetes"   "totChol"   
## [11] "sysBP"      "diaBP"      "BMI"        "heartRate"  "glucose"   
## [16] "TenYearCHD"
data %>% 
  select_if(is.numeric) %>% 
  gather %>% 
  ggplot(aes(x = value)) + facet_wrap(~ key, scales = "free", nrow = 3) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 645 rows containing non-finite values (stat_bin).

data %>% 
  select_if(is.numeric) %>% 
  cor
##                    male         age education   curSmoker cigsPerDay
## male        1.000000000 -0.02901358        NA  0.19702562         NA
## age        -0.029013582  1.00000000        NA -0.21366166         NA
## education            NA          NA         1          NA         NA
## curSmoker   0.197025619 -0.21366166        NA  1.00000000         NA
## cigsPerDay           NA          NA        NA          NA          1
## BPMeds               NA          NA        NA          NA         NA
## prevStroke -0.004550399  0.05767861        NA -0.03298039         NA
## prevHyp     0.005852836  0.30679947        NA -0.10371030         NA
## diabetes    0.015693075  0.10131408        NA -0.04428530         NA
## totChol              NA          NA        NA          NA         NA
## sysBP      -0.035879033  0.39405332        NA -0.13028149         NA
## diaBP       0.058199421  0.20558552        NA -0.10793319         NA
## BMI                  NA          NA        NA          NA         NA
## heartRate            NA          NA        NA          NA         NA
## glucose              NA          NA        NA          NA         NA
## TenYearCHD  0.088373572  0.22540774        NA  0.01944850         NA
##            BPMeds   prevStroke      prevHyp     diabetes totChol
## male           NA -0.004550399  0.005852836  0.015693075      NA
## age            NA  0.057678613  0.306799467  0.101314077      NA
## education      NA           NA           NA           NA      NA
## curSmoker      NA -0.032980386 -0.103710297 -0.044285298      NA
## cigsPerDay     NA           NA           NA           NA      NA
## BPMeds          1           NA           NA           NA      NA
## prevStroke     NA  1.000000000  0.074791128  0.006955094      NA
## prevHyp        NA  0.074791128  1.000000000  0.077752047      NA
## diabetes       NA  0.006955094  0.077752047  1.000000000      NA
## totChol        NA           NA           NA           NA       1
## sysBP          NA  0.056999937  0.696655883  0.111264543      NA
## diaBP          NA  0.045153466  0.615840200  0.050260378      NA
## BMI            NA           NA           NA           NA      NA
## heartRate      NA           NA           NA           NA      NA
## glucose        NA           NA           NA           NA      NA
## TenYearCHD     NA  0.061822628  0.177457561  0.097344236      NA
##                  sysBP       diaBP BMI heartRate glucose TenYearCHD
## male       -0.03587903  0.05819942  NA        NA      NA 0.08837357
## age         0.39405332  0.20558552  NA        NA      NA 0.22540774
## education           NA          NA  NA        NA      NA         NA
## curSmoker  -0.13028149 -0.10793319  NA        NA      NA 0.01944850
## cigsPerDay          NA          NA  NA        NA      NA         NA
## BPMeds              NA          NA  NA        NA      NA         NA
## prevStroke  0.05699994  0.04515347  NA        NA      NA 0.06182263
## prevHyp     0.69665588  0.61584020  NA        NA      NA 0.17745756
## diabetes    0.11126454  0.05026038  NA        NA      NA 0.09734424
## totChol             NA          NA  NA        NA      NA         NA
## sysBP       1.00000000  0.78395196  NA        NA      NA 0.21637383
## diaBP       0.78395196  1.00000000  NA        NA      NA 0.14511159
## BMI                 NA          NA   1        NA      NA         NA
## heartRate           NA          NA  NA         1      NA         NA
## glucose             NA          NA  NA        NA       1         NA
## TenYearCHD  0.21637383  0.14511159  NA        NA      NA 1.00000000
data %>% 
  select_if(is.numeric) %>% 
  gather %>% 
  ggplot(aes(x = 1, y = value)) + facet_wrap(~ key, scales = "free") + 
  geom_boxplot(color="steelblue") +
  ylab("Value") +
  xlab("Variable")
## Warning: Removed 645 rows containing non-finite values (stat_boxplot).

data %>%
  #select_if(negate(is.numeric)) %>%
  #select(-matches("essay")) %>%
  gather %>%
  ggplot(aes(x = value)) + geom_bar(fill="steelblue",color="steelblue") +
  facet_wrap(~ key, scales = "free", ncol = 3)
## Warning: Removed 645 rows containing non-finite values (stat_count).

data %>% 
  select_if(is.numeric) %>% 
  map(summary) 
## $male
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.4292  1.0000  1.0000 
## 
## $age
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   32.00   42.00   49.00   49.58   56.00   70.00 
## 
## $education
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   1.000   2.000   1.979   3.000   4.000     105 
## 
## $curSmoker
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.4941  1.0000  1.0000 
## 
## $cigsPerDay
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   9.006  20.000  70.000      29 
## 
## $BPMeds
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## 0.00000 0.00000 0.00000 0.02962 0.00000 1.00000      53 
## 
## $prevStroke
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## 0.000000 0.000000 0.000000 0.005896 0.000000 1.000000 
## 
## $prevHyp
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.3106  1.0000  1.0000 
## 
## $diabetes
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.00000 0.00000 0.02571 0.00000 1.00000 
## 
## $totChol
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   107.0   206.0   234.0   236.7   263.0   696.0      50 
## 
## $sysBP
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    83.5   117.0   128.0   132.4   144.0   295.0 
## 
## $diaBP
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    48.0    75.0    82.0    82.9    90.0   142.5 
## 
## $BMI
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   15.54   23.07   25.40   25.80   28.04   56.80      19 
## 
## $heartRate
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   44.00   68.00   75.00   75.88   83.00  143.00       1 
## 
## $glucose
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   40.00   71.00   78.00   81.96   87.00  394.00     388 
## 
## $TenYearCHD
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.1519  0.0000  1.0000
#missingFrame <- apply(is.na(data), 2, which)

#missingFrame$glucose
#which(complete.cases(data) == FALSE)

sum(is.na(data))
## [1] 645
data %>% 
  select_if(is.numeric) %>% 
  cor.plot(numbers = TRUE, main="Correlation Plot of CHD Dataset",diag=FALSE)

KMO(data)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data)
## Overall MSA =  0.69
## MSA for each item = 
##       male        age  education  curSmoker cigsPerDay     BPMeds 
##       0.54       0.73       0.65       0.57       0.55       0.86 
## prevStroke    prevHyp   diabetes    totChol      sysBP      diaBP 
##       0.70       0.87       0.55       0.77       0.71       0.72 
##        BMI  heartRate    glucose TenYearCHD 
##       0.85       0.71       0.55       0.83
prop.table(table(data$diabetes,data$TenYearCHD),1)*100
##    
##            0        1
##   0 85.37884 14.62116
##   1 63.30275 36.69725
prop.table(table(data$prevStroke,data$TenYearCHD),1)*100
##    
##            0        1
##   0 84.98221 15.01779
##   1 56.00000 44.00000
prop.table(table(data$prevHyp,data$TenYearCHD),1)*100
##    
##            0        1
##   0 89.08655 10.91345
##   1 75.32270 24.67730
prop.table(table(data$BPMeds,data$TenYearCHD),1)*100
##    
##            0        1
##   0 85.42949 14.57051
##   1 66.93548 33.06452

Exploratory data analysis

data1 <- data

data1$education <- as.character( 
    factor( 
      data1$education, 
      levels = c("1", "2","3","4"), 
      labels = c("1","2","3","4"))) 

data1$male <- as.character( 
    factor( 
      data1$male, 
      levels = c("0", "1"), 
      labels = c("Female", "Male"))) 

data1$TenYearCHD <- as.character( 
    factor( 
      data1$TenYearCHD, 
      levels = c("0", "1"), 
      labels = c("No", "Yes"))) 

data1$BPMeds <- as.character( 
    factor( 
      data1$BPMeds, 
      levels = c("0", "1"), 
      labels = c("No", "Yes"))) 

data1$prevStroke <- as.character( 
    factor( 
      data1$prevStroke, 
      levels = c("0", "1"), 
      labels = c("No", "Yes"))) 

data1$prevHyp <- as.character( 
    factor( 
      data1$prevHyp, 
      levels = c("0", "1"), 
      labels = c("No", "Yes"))) 

data1$diabetes <- as.character( 
    factor( 
      data1$diabetes, 
      levels = c("0", "1"), 
      labels = c("No", "Yes"))) 

data1$curSmoker <- as.character( 
    factor( 
      data1$curSmoker, 
      levels = c("0", "1"), 
      labels = c("No", "Yes"))) 

cutage <- cut(data1$age,breaks=c(30,40,50,60,70,Inf),labels=c("30-40","40-50","50-60","60-70","55to65"))

#Age distribution among M/F employees
ggplot(data1)+geom_bar(aes(x=cutage,fill=as.factor(male)))+theme_bw()+scale_fill_discrete(name="Gender")+
  labs(
        x = "Age",
        title = paste(
            "Male/Female among Age groups"
        ))

educationFilter <- filter(data1,education %in% c("1","2","3","4"))
cutageedu <- cut(educationFilter$age,breaks=c(30,40,50,60,70,Inf),labels=c("30-40","40-50","50-60","60-70","55to65"))

#Education among age groups
ggplot(educationFilter)+geom_bar(aes(x=cutageedu,fill=as.factor(education)))+theme_bw()+scale_fill_discrete(name="Education")+
  labs(
        x = "Age",
        title = paste(
            "Education among age groups"
        ))

#Gender distribution vs Coronary heart disease
ggplot(data1)+geom_bar(aes(x=TenYearCHD,fill=as.factor(male)))+scale_fill_discrete(name="Gender")+theme_bw()+labs(
        x = "CHD Suspect",
        #y = "Male Vs Female",
        title = paste(
            "Gender distribution among CHD Suspects"
        )) 

#Age wise CHD suspects
ggplot(data1)+geom_bar(aes(x=cutage,fill=as.factor(TenYearCHD)))+theme_bw()+scale_fill_discrete(name="CHD Suspect")+
  labs(
        x = "Age",
        title = paste(
            "Agewise CHD Suspects "
        ))

#Genderwise Smokers
p1 <- ggplot(data1)+geom_bar(aes(x=male,fill=as.factor(curSmoker)))+theme_bw()+scale_fill_discrete(name="Smokers")+scale_x_discrete(name="Gender")+labs(title = "Genderwise Smokers")

#Age wise Smokers  
p2<-ggplot(data1)+geom_bar(aes(x=cutage,fill=as.factor(curSmoker)))+theme_bw()+scale_fill_discrete(name="Smokers")+
  labs(
        x = "Age",
        title = paste(
            "Smokers among Age group "
        ))
grid.arrange(p1,p2)

#Diabetes among age group
p3<-ggplot(data1)+geom_bar(aes(x=cutage,fill=as.factor(diabetes)))+theme_bw()+scale_fill_discrete(name="Diabetes")+
  labs(
        x = "Age",
        title = paste(
            "Diabetes among age group "
        ))
#Prevalent Stroke among age group
p4<-ggplot(data1)+geom_bar(aes(x=cutage,fill=as.factor(prevStroke)))+theme_bw()+scale_fill_discrete(name="Stroke")+
  labs(
        x = "Age",
        title = paste(
            "Stroke among age group "
        ))

#Prevalent stroke among Smokers  
p5<-ggplot(data1)+geom_bar(aes(x=diabetes,fill=as.factor(curSmoker)))+theme_bw()+scale_fill_discrete(name="Smokers")+
  labs(
        x = "Diabetes",
        title = paste(
            "Diabetes among Smokers"
        ))

#Diabetes among Smokers  
p6<-ggplot(data1)+geom_bar(aes(x=prevStroke,fill=as.factor(curSmoker)))+theme_bw()+scale_fill_discrete(name="Smokers")+labs(x = "Prevalent Stroke",title = paste("Stroke among Smokers"))

grid.arrange(p3,p5,p4,p6,ncol=2, nrow=2)

bpmedfilter <- filter(data1,BPMeds %in% c("Yes","No"))

cutagebp <- cut(bpmedfilter$age,breaks=c(30,40,50,60,70,Inf),labels=c("30-40","40-50","50-60","60-70","55to65"))

#BP Medication among age group
ggplot(bpmedfilter)+geom_bar(aes(x=cutagebp,fill=as.factor(BPMeds)))+theme_bw()+scale_fill_discrete(name="BP Medication")+
  labs(
        x = "Age",
        title = paste(
            "BP Medication among age group "
        ))

#Diabetes among age group
ggplot(data1)+geom_bar(aes(x=cutage,fill=as.factor(diabetes)))+theme_bw()+scale_fill_discrete(name="Diabetes")+
  labs(
        x = "Age",
        title = paste(
            "Diabetes among age group "
        ))

#Education among age group
ggplot(data1)+geom_bar(aes(x=cutage,fill=as.factor(education)))+theme_bw()+scale_fill_discrete(name="Education")+
  labs(
        x = "Age",
        title = paste(
            "Education among age group "
        ))

cholFilter <- filter(data1,totChol %in% (1:800))

cutChol <- cut(cholFilter$totChol,breaks=c(100,200,300,400,500,600,700),labels=c("0-100","100-200","200-300","300-400","500-600",">600"))

#Cholestoral among CHD Suspects 
ggplot(cholFilter)+geom_bar(aes(x=cutChol,fill=as.factor(TenYearCHD)))+theme_bw()+
  scale_fill_discrete(name="CHD Suspects")+
  labs(
        #y = "Age",
        x= "Tot Cholestoral",
        title = paste("Cholestoral Vs CHD Suspects"))

#Glucose vs sysBP
ggplot(data1, aes(x=glucose, y=sysBP,color=male)) +geom_point(shape=18) + geom_smooth(method=lm,fullrange=TRUE,se=FALSE)+geom_density2d()+stat_density_2d(aes(fill = ..level..), geom="polygon")
## Warning: Removed 388 rows containing non-finite values (stat_smooth).
## Warning: Removed 388 rows containing non-finite values (stat_density2d).

## Warning: Removed 388 rows containing non-finite values (stat_density2d).
## Warning: Removed 388 rows containing missing values (geom_point).

#+geom_text(label=rownames(data))

# Glucose vs diaBP
ggplot(data1, aes(x=glucose, y=diaBP,color=male)) +geom_point(shape=18) + geom_smooth(method=lm,fullrange=TRUE,se=FALSE)
## Warning: Removed 388 rows containing non-finite values (stat_smooth).

## Warning: Removed 388 rows containing missing values (geom_point).

#Age vs Tot Chol
cutagechol <- cut(cholFilter$age,breaks=c(30,40,50,60,70,Inf),labels=c("30-40","40-50","50-60","60-70","55to65"))
ggplot(cholFilter, aes(x=age, y=totChol,color=male)) +geom_point(shape=18) + geom_smooth(method=lm,fullrange=TRUE,se=FALSE)#+stat_ellipse(type = "norm")

#Heartrate vs Total Cholestoral
ggplot(cholFilter, aes(x=heartRate, y=totChol)) +geom_point(shape=18, color="steelblue") + geom_smooth(method=lm,fullrange=TRUE,se=FALSE)+stat_ellipse(type = "norm")
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing non-finite values (stat_ellipse).
## Warning: Removed 1 rows containing missing values (geom_point).

#Systolic BP vs CHD Suspect
ggplot(data1, aes(glucose, fill=TenYearCHD)) + geom_density(alpha=.5) + 
  scale_fill_manual(values = c('#999999','#E69F00')) + theme(legend.position = "right")+scale_fill_discrete(name="CHD Suspects")
## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.
## Warning: Removed 388 rows containing non-finite values (stat_density).

#Diastolic BP vs CHD suspect
ggplot(data1, aes(totChol, fill=TenYearCHD)) + geom_density(alpha=.5) + 
  scale_fill_manual(values = c('#999999','#E69F00')) + theme(legend.position = "right")+scale_fill_discrete(name="CHD Suspects")
## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.
## Warning: Removed 50 rows containing non-finite values (stat_density).

#Cholestoral among CHD Suspects 
ggplot(cholFilter)+geom_bar(aes(x=cutagechol,fill=as.factor(cutChol)))+theme_bw()+
  scale_fill_discrete(name="Cholestoral")+
  labs(
        #y = "Age",
        x= "Age",
        title = paste("Age Vs Cholestoral"))

#Area plot for Total Cholestoral against Gender
ggplot(data1,aes(x=totChol))+geom_area(aes(fill = male),stat ="bin", alpha=0.6) +
  theme_classic()+labs(title="Total Cholestoral among Gender")+scale_fill_discrete(name="Gender")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 50 rows containing non-finite values (stat_bin).

#Density plot of Tot cholestoral among Gender
ggplot(data1,aes(x=totChol))+geom_density(aes(color = male)) +
  geom_vline(data=data1, aes(xintercept=mean(totChol), color=male),
             linetype="dashed") +
  scale_color_manual(values=c("#E69F00", "steelblue"))+scale_fill_discrete(name="Gender")
## Warning: Removed 50 rows containing non-finite values (stat_density).
## Warning: Removed 4240 rows containing missing values (geom_vline).

#Density plot of Glucose among Gender
ggplot(data1,aes(x=glucose))+geom_density(aes(color = male)) +
geom_vline(data=data1, aes(xintercept=mean(glucose), color=male),
linetype="dashed") +scale_color_manual(values=c("#E69F00", "steelblue"))+
scale_fill_discrete(name="Gender")
## Warning: Removed 388 rows containing non-finite values (stat_density).

## Warning: Removed 4240 rows containing missing values (geom_vline).

ggplot(data1,aes(x=glucose))+geom_dotplot(aes(fill=TenYearCHD))+scale_fill_discrete(name="CHD Suspect")
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 388 rows containing non-finite values (stat_bindot).

ggplot(data1,aes(x=diaBP))+stat_bin(bindwidth=15)+geom_dotplot(aes(fill=(TenYearCHD="Yes")))+scale_fill_discrete(name="CHD Suspect")
## Warning: Ignoring unknown parameters: bindwidth
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(educationFilter,aes(x=glucose))+stat_bin(bindwidth=15)+geom_dotplot(aes(fill=education))
## Warning: Ignoring unknown parameters: bindwidth
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 380 rows containing non-finite values (stat_bin).
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 380 rows containing non-finite values (stat_bindot).

ggplot(educationFilter,aes(x=totChol))+stat_bin(bindwidth=15)+geom_dotplot(aes(fill=education))
## Warning: Ignoring unknown parameters: bindwidth
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 49 rows containing non-finite values (stat_bin).
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 49 rows containing non-finite values (stat_bindot).

  1. Treating for outliers (5%-95%) capping and mice imputation of NAs

# pcap <- function(x){
#   for (i in which(sapply(x, is.numeric))) {
#   quantiles <- quantile( x[,i], c(.05, .95 ), na.rm =TRUE)
#   x[,i] = ifelse(x[,i] < quantiles[1] , quantiles[1], x[,i])
#   x[,i] = ifelse(x[,i] > quantiles[2] , quantiles[2], x[,i])}
#   x}
# datacap <- data
# datacap$male <- as.factor(datacap$male)
# datacap$education <- as.factor(datacap$education)
# datacap$curSmoker <- as.factor(datacap$curSmoker)
# datacap$prevStroke <- as.factor(datacap$prevStroke)
# datacap$prevHyp <- as.factor(datacap$prevHyp)
# datacap$diabetes <- as.factor(datacap$diabetes)
# datacap$BPMeds <- as.factor(datacap$BPMeds)
# datacap$TenYearCHD <- as.factor(datacap$TenYearCHD)
# 
# 
# abcd <- pcap(datacap)
# 
# quantile(abcd[,15], c(0.25,0.5,.95, .99, 1), na.rm = TRUE)
# 
# #abcd$TenYearCHD <- as.factor(abcd$TenYearCHD)
# 
# miceModab <- mice(abcd[,!names(abcd) %in% 'TenYearCHD'],seed = 500,maxit = 20,printFlag = FALSE)
# miceOutputab <- complete(miceModab)
# anyNA(miceOutputab)
# 
# miceOutputab1 <- cbind(miceOutputab,data$TenYearCHD)
# miceOutputab1 <- dplyr::rename(miceOutputab1,"TenYearCHD"="data$TenYearCHD")
# 
# miceOutputab1$TenYearCHD <- as.factor(miceOutputab1$TenYearCHD)
# 
# dfCHDModel1 <- miceOutputab1
# 
# densityplot(miceModab)
  1. Using dlookr package for Outlier treatment + imputing NAs

data2 <- data

data2$male <- as.factor(data2$male)
data2$prevStroke <- as.factor(data2$prevStroke)
data2$prevHyp <- as.factor(data2$prevHyp)
data2$education <- as.factor(data2$education)
data2$BPMeds <- as.factor(data2$BPMeds)
data2$diabetes <- as.factor(data2$diabetes)
data2$curSmoker <- as.factor(data2$curSmoker)
data2$TenYearCHD <- as.factor(data2$TenYearCHD)

starttime <- Sys.time()
######Glucose#########################################
glucose1 <- imputate_outlier(data2,glucose,method ="capping")
## Warning: Unquoting language objects with `!!!` is deprecated as of rlang 0.4.0.
## Please use `!!` instead.
## 
##   # Bad:
##   dplyr::select(data, !!!enquo(x))
## 
##   # Good:
##   dplyr::select(data, !!enquo(x))    # Unquote single quosure
##   dplyr::select(data, !!!enquos(x))  # Splice list of quosures
## 
## This warning is displayed once per session.
summary(glucose1)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## Impute outliers with capping
## 
## * Information of Imputation (before vs after)
##              Original    Imputation
## n        3852.0000000 3852.00000000
## na        388.0000000  388.00000000
## mean       81.9636552   79.84230270
## sd         23.9543348   12.48923977
## se_mean     0.3859588    0.20123006
## IQR        16.0000000   16.00000000
## skewness    6.2149483    0.55538718
## kurtosis   58.7037414   -0.04218054
## p00        40.0000000   47.00000000
## p01        55.0000000   57.00000000
## p05        62.0000000   62.00000000
## p10        65.0000000   65.00000000
## p20        70.0000000   70.00000000
## p25        71.0000000   71.00000000
## p30        73.0000000   73.00000000
## p40        75.0000000   75.00000000
## p50        78.0000000   78.00000000
## p60        81.0000000   81.00000000
## p70        85.0000000   85.00000000
## p75        87.0000000   87.00000000
## p80        89.0000000   89.00000000
## p90        98.0000000   98.00000000
## p95       108.4500000  108.20250000
## p99       174.9600000  108.45000000
## p100      394.0000000  111.00000000
plot(glucose1)

data2 <- cbind(data2,glucose1)

data2$glucose1 <- as.numeric(data2$glucose1)

data2 <- data2[,c(-15)]


glucose2 <- imputate_na(data2,glucose1,method = "mice",print_flag = FALSE)

summary(glucose2)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## * Information of Imputation (before vs after)
##               Original   Imputation
## n        3852.00000000 4240.0000000
## na        388.00000000    0.0000000
## mean       79.84230270   79.8456792
## sd         12.48923977   12.0277904
## se_mean     0.20123006    0.1847153
## IQR        16.00000000   14.0000000
## skewness    0.55538718    0.5699037
## kurtosis   -0.04218054    0.1409208
## p00        47.00000000   47.0000000
## p01        57.00000000   57.0000000
## p05        62.00000000   62.0000000
## p10        65.00000000   66.0000000
## p20        70.00000000   70.0000000
## p25        71.00000000   72.0000000
## p30        73.00000000   73.0000000
## p40        75.00000000   76.0000000
## p50        78.00000000   78.0000000
## p60        81.00000000   81.0000000
## p70        85.00000000   84.0000000
## p75        87.00000000   86.0000000
## p80        89.00000000   88.0000000
## p90        98.00000000   97.0000000
## p95       108.20250000  107.0000000
## p99       108.45000000  108.4500000
## p100      111.00000000  111.0000000
plot(glucose2)

data2 <- cbind(data2,glucose2)

data2$glucose2 <- as.numeric(data2$glucose2)

data2 <- data2[,c(-16)]


data2<-dplyr::rename(data2,"glucose"="glucose2")

###################Education#############################################

data2$education <- as.factor(data2$education)

education1 <- imputate_na(data2,education,method = "mice",seed = 100, print_flag = FALSE)

summary(education1)
## * Impute missing values based on Multivariate Imputation by Chained Equations
##  - method : mice
##  - random seed : 100
## 
## * Information of Imputation (before vs after)
##      original imputation original_percent imputation_percent
## 1        1720       1768            40.57              41.70
## 2        1253       1280            29.55              30.19
## 3         689        707            16.25              16.67
## 4         473        485            11.16              11.44
## <NA>      105          0             2.48               0.00
plot(education1)

data2 <- cbind(data2,education1)

data2$education1 <- as.factor(data2$education1)

data2 <- data2[,c(-3)]


data2<-dplyr::rename(data2,"education"="education1")
#######################BMI########################################
bmi1 <- imputate_outlier(data2,BMI,method ="capping")

summary(bmi1)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## Impute outliers with capping
## 
## * Information of Imputation (before vs after)
##              Original    Imputation
## n        4.221000e+03 4221.00000000
## na       1.900000e+01   19.00000000
## mean     2.580080e+01   25.65549159
## sd       4.079840e+00    3.65593642
## se_mean  6.279651e-02    0.05627182
## IQR      4.970000e+00    4.97000000
## skewness 9.821833e-01    0.27394434
## kurtosis 2.657310e+00   -0.31002603
## p00      1.554000e+01   15.96000000
## p01      1.816400e+01   18.18000000
## p05      2.006000e+01   20.06000000
## p10      2.108000e+01   21.08000000
## p20      2.253000e+01   22.53000000
## p25      2.307000e+01   23.07000000
## p30      2.356000e+01   23.56000000
## p40      2.447000e+01   24.47000000
## p50      2.540000e+01   25.40000000
## p60      2.635000e+01   26.35000000
## p70      2.742000e+01   27.42000000
## p75      2.804000e+01   28.04000000
## p80      2.869000e+01   28.69000000
## p90      3.077000e+01   30.77000000
## p95      3.278000e+01   32.78000000
## p99      3.895600e+01   34.39800000
## p100     5.680000e+01   35.45000000
plot(bmi1)

data2 <- cbind(data2,bmi1)

data2$bmi1 <- as.numeric(data2$bmi1)

data2 <- data2[,c(-12)]


bmi2 <- imputate_na(data2,bmi1,method = "mice",seed=100,print_flag = FALSE)

summary(bmi2)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## * Impute missing values based on Multivariate Imputation by Chained Equations
##  - method : mice
##  - random seed : 100
## 
## * Information of Imputation (before vs after)
##               Original   Imputation
## n        4221.00000000 4240.0000000
## na         19.00000000    0.0000000
## mean       25.65549159   25.6548396
## sd          3.65593642    3.6516846
## se_mean     0.05627182    0.0560803
## IQR         4.97000000    4.9700000
## skewness    0.27394434    0.2745537
## kurtosis   -0.31002603   -0.3072472
## p00        15.96000000   15.9600000
## p01        18.18000000   18.1800000
## p05        20.06000000   20.0600000
## p10        21.08000000   21.0990000
## p20        22.53000000   22.5300000
## p25        23.07000000   23.0700000
## p30        23.56000000   23.5700000
## p40        24.47000000   24.4700000
## p50        25.40000000   25.3850000
## p60        26.35000000   26.3500000
## p70        27.42000000   27.4200000
## p75        28.04000000   28.0400000
## p80        28.69000000   28.6900000
## p90        30.77000000   30.7610000
## p95        32.78000000   32.7705000
## p99        34.39800000   34.3961000
## p100       35.45000000   35.4500000
plot(bmi2)

data2 <- cbind(data2,bmi2)

data2$bmi2 <- as.numeric(data2$bmi2)

data2 <- data2[,c(-16)]


data2<-dplyr::rename(data2,"BMI"="bmi2")
boxplot(data2$BMI)

#######################################

sysBP1 <- imputate_outlier(data2,sysBP,method ="capping")

summary(sysBP1)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## Impute outliers with capping
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4240.0000000 4240.0000000
## na          0.0000000    0.0000000
## mean      132.3545991  131.6333726
## sd         22.0332996   19.9758159
## se_mean     0.3383737    0.3067762
## IQR        27.0000000   27.0000000
## skewness    1.1452850    0.6063585
## kurtosis    2.1566236   -0.2295674
## p00        83.5000000   83.5000000
## p01        97.0000000   97.0000000
## p05       104.0000000  104.0000000
## p10       108.9500000  108.9500000
## p20       114.0000000  114.0000000
## p25       117.0000000  117.0000000
## p30       119.5000000  119.5000000
## p40       124.0000000  124.0000000
## p50       128.0000000  128.0000000
## p60       133.0000000  133.0000000
## p70       140.0000000  140.0000000
## p75       144.0000000  144.0000000
## p80       148.0000000  148.0000000
## p90       162.0000000  162.0000000
## p95       175.0000000  175.0000000
## p99       200.0000000  180.0000000
## p100      295.0000000  184.5000000
plot(sysBP1)

data2 <- cbind(data2,sysBP1)

data2$sysBP1 <- as.numeric(data2$sysBP1)

data2 <- data2[,c(-10)]


sysBP2 <- imputate_na(data2,sysBP1,method = "mice",seed=100,print_flag = FALSE)
## Warning in imputate_na_impl(.data, vars, target, method, seed,
## print_flag, : There are no missing values in sysBP1.
summary(sysBP2)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## * Impute missing values based on Multivariate Imputation by Chained Equations
##  - method : mice
##  - random seed : 100
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4240.0000000 4240.0000000
## na          0.0000000    0.0000000
## mean      131.6333726  131.6333726
## sd         19.9758159   19.9758159
## se_mean     0.3067762    0.3067762
## IQR        27.0000000   27.0000000
## skewness    0.6063585    0.6063585
## kurtosis   -0.2295674   -0.2295674
## p00        83.5000000   83.5000000
## p01        97.0000000   97.0000000
## p05       104.0000000  104.0000000
## p10       108.9500000  108.9500000
## p20       114.0000000  114.0000000
## p25       117.0000000  117.0000000
## p30       119.5000000  119.5000000
## p40       124.0000000  124.0000000
## p50       128.0000000  128.0000000
## p60       133.0000000  133.0000000
## p70       140.0000000  140.0000000
## p75       144.0000000  144.0000000
## p80       148.0000000  148.0000000
## p90       162.0000000  162.0000000
## p95       175.0000000  175.0000000
## p99       180.0000000  180.0000000
## p100      184.5000000  184.5000000
plot(sysBP2)

data2 <- cbind(data2,sysBP2)

data2$sysBP2 <- as.numeric(data2$sysBP2)

data2 <- data2[,c(-16)]


data2<-dplyr::rename(data2,"sysBP"="sysBP2")
boxplot(data2$sysBP)

##################DiaBP#####################
diaBP1 <- imputate_outlier(data2,diaBP,method ="capping")

summary(diaBP1)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## Impute outliers with capping
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4240.0000000 4240.0000000
## na          0.0000000    0.0000000
## mean       82.8977594   82.6235377
## sd         11.9103945   11.0600511
## se_mean     0.1829124    0.1698534
## IQR        15.0000000   15.0000000
## skewness    0.7132502    0.3016647
## kurtosis    1.2753143   -0.2425547
## p00        48.0000000   53.0000000
## p01        60.0000000   60.0000000
## p05        66.0000000   66.0000000
## p10        69.0000000   69.0000000
## p20        73.0000000   73.0000000
## p25        75.0000000   75.0000000
## p30        76.0000000   76.0000000
## p40        80.0000000   80.0000000
## p50        82.0000000   82.0000000
## p60        85.0000000   85.0000000
## p70        87.5000000   87.5000000
## p75        90.0000000   90.0000000
## p80        92.0000000   92.0000000
## p90        98.0000000   98.0000000
## p95       104.5250000  104.5012500
## p99       118.0000000  109.0000000
## p100      142.5000000  112.5000000
plot(diaBP1)

data2 <- cbind(data2,diaBP1)

data2$diaBP1 <- as.numeric(data2$diaBP1)

data2 <- data2[,c(-10)]


diaBP2 <- imputate_na(data2,diaBP1,method = "mice",seed=100,print_flag = FALSE)
## Warning in imputate_na_impl(.data, vars, target, method, seed,
## print_flag, : There are no missing values in diaBP1.
summary(diaBP2)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## * Impute missing values based on Multivariate Imputation by Chained Equations
##  - method : mice
##  - random seed : 100
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4240.0000000 4240.0000000
## na          0.0000000    0.0000000
## mean       82.6235377   82.6235377
## sd         11.0600511   11.0600511
## se_mean     0.1698534    0.1698534
## IQR        15.0000000   15.0000000
## skewness    0.3016647    0.3016647
## kurtosis   -0.2425547   -0.2425547
## p00        53.0000000   53.0000000
## p01        60.0000000   60.0000000
## p05        66.0000000   66.0000000
## p10        69.0000000   69.0000000
## p20        73.0000000   73.0000000
## p25        75.0000000   75.0000000
## p30        76.0000000   76.0000000
## p40        80.0000000   80.0000000
## p50        82.0000000   82.0000000
## p60        85.0000000   85.0000000
## p70        87.5000000   87.5000000
## p75        90.0000000   90.0000000
## p80        92.0000000   92.0000000
## p90        98.0000000   98.0000000
## p95       104.5012500  104.5012500
## p99       109.0000000  109.0000000
## p100      112.5000000  112.5000000
plot(diaBP2)

data2 <- cbind(data2,diaBP2)

data2$diaBP2 <- as.numeric(data2$diaBP2)

data2 <- data2[,c(-16)]


data2<-dplyr::rename(data2,"diaBP"="diaBP2")

boxplot(data2$diaBP)

###################heartRate####################

heartRate1 <- imputate_outlier(data2,heartRate,method ="capping")

summary(heartRate1)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## Impute outliers with capping
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4239.0000000 4239.0000000
## na          1.0000000    1.0000000
## mean       75.8789809   75.6319887
## sd         12.0253480   11.3095146
## se_mean     0.1846996    0.1737050
## IQR        15.0000000   15.0000000
## skewness    0.6443718    0.2984974
## kurtosis    0.9073957   -0.3435792
## p00        44.0000000   46.0000000
## p01        52.0000000   52.0000000
## p05        60.0000000   60.0000000
## p10        60.0000000   60.0000000
## p20        65.0000000   65.0000000
## p25        68.0000000   68.0000000
## p30        70.0000000   70.0000000
## p40        72.0000000   72.0000000
## p50        75.0000000   75.0000000
## p60        77.0000000   77.0000000
## p70        80.0000000   80.0000000
## p75        83.0000000   83.0000000
## p80        85.0000000   85.0000000
## p90        92.0000000   92.0000000
## p95        98.0000000   98.0000000
## p99       110.0000000  100.0000000
## p100      143.0000000  105.0000000
plot(heartRate1)

data2 <- cbind(data2,heartRate1)

data2$heartRate1 <- as.numeric(data2$heartRate1)

data2 <- data2[,c(-10)]


heartRate2 <- imputate_na(data2,heartRate1,method = "mice",seed=100,print_flag = FALSE)

summary(heartRate2)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## * Impute missing values based on Multivariate Imputation by Chained Equations
##  - method : mice
##  - random seed : 100
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4239.0000000 4240.0000000
## na          1.0000000    0.0000000
## mean       75.6319887   75.6329245
## sd         11.3095146   11.3083447
## se_mean     0.1737050    0.1736665
## IQR        15.0000000   15.0000000
## skewness    0.2984974    0.2982814
## kurtosis   -0.3435792   -0.3432020
## p00        46.0000000   46.0000000
## p01        52.0000000   52.0000000
## p05        60.0000000   60.0000000
## p10        60.0000000   60.0000000
## p20        65.0000000   65.0000000
## p25        68.0000000   68.0000000
## p30        70.0000000   70.0000000
## p40        72.0000000   72.0000000
## p50        75.0000000   75.0000000
## p60        77.0000000   77.0000000
## p70        80.0000000   80.0000000
## p75        83.0000000   83.0000000
## p80        85.0000000   85.0000000
## p90        92.0000000   92.0000000
## p95        98.0000000   98.0000000
## p99       100.0000000  100.0000000
## p100      105.0000000  105.0000000
plot(heartRate2)

data2 <- cbind(data2,heartRate2)

data2$heartRate2 <- as.numeric(data2$heartRate2)

data2 <- data2[,c(-16)]


data2<-dplyr::rename(data2,"heartRate"="heartRate2")

boxplot(data2$heartRate)

#############################################################
###################cigsPerDay####################

cigsPerDay1 <- imputate_outlier(data2,cigsPerDay,method ="capping")

summary(cigsPerDay1)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## Impute outliers with capping
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4211.0000000 4211.0000000
## na         29.0000000   29.0000000
## mean        9.0059368    8.9180717
## sd         11.9224618   11.6497045
## se_mean     0.1837271    0.1795239
## IQR        20.0000000   20.0000000
## skewness    1.2470524    1.1226870
## kurtosis    1.0194182    0.3337949
## p00         0.0000000    0.0000000
## p01         0.0000000    0.0000000
## p05         0.0000000    0.0000000
## p10         0.0000000    0.0000000
## p20         0.0000000    0.0000000
## p25         0.0000000    0.0000000
## p30         0.0000000    0.0000000
## p40         0.0000000    0.0000000
## p50         0.0000000    0.0000000
## p60         9.0000000    9.0000000
## p70        15.0000000   15.0000000
## p75        20.0000000   20.0000000
## p80        20.0000000   20.0000000
## p90        25.0000000   25.0000000
## p95        30.0000000   30.0000000
## p99        43.0000000   43.0000000
## p100       70.0000000   50.0000000
plot(cigsPerDay1)

data2 <- cbind(data2,cigsPerDay1)

data2$cigsPerDay1 <- as.numeric(data2$cigsPerDay1)

data2 <- data2[,c(-4)]


cigsPerDay2 <- imputate_na(data2,cigsPerDay1,method = "mice",seed=100,print_flag = FALSE)

summary(cigsPerDay2)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## * Impute missing values based on Multivariate Imputation by Chained Equations
##  - method : mice
##  - random seed : 100
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4211.0000000 4240.0000000
## na         29.0000000    0.0000000
## mean        8.9180717    8.9824528
## sd         11.6497045   11.6405857
## se_mean     0.1795239    0.1787689
## IQR        20.0000000   20.0000000
## skewness    1.1226870    1.1064888
## kurtosis    0.3337949    0.3025172
## p00         0.0000000    0.0000000
## p01         0.0000000    0.0000000
## p05         0.0000000    0.0000000
## p10         0.0000000    0.0000000
## p20         0.0000000    0.0000000
## p25         0.0000000    0.0000000
## p30         0.0000000    0.0000000
## p40         0.0000000    0.0000000
## p50         0.0000000    0.0000000
## p60         9.0000000    9.0000000
## p70        15.0000000   15.0000000
## p75        20.0000000   20.0000000
## p80        20.0000000   20.0000000
## p90        25.0000000   25.0000000
## p95        30.0000000   30.0000000
## p99        43.0000000   43.0000000
## p100       50.0000000   50.0000000
plot(cigsPerDay2)

data2 <- cbind(data2,cigsPerDay2)

data2$cigsPerDay2 <- as.numeric(data2$cigsPerDay2)

data2 <- data2[,c(-16)]


data2<-dplyr::rename(data2,"cigsPerDay"="cigsPerDay2")

boxplot(data2$cigsPerDay)

#############################################################

###################tot Cholesterol####################

totChol1 <- imputate_outlier(data2,totChol,method ="capping")

summary(totChol1)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## Impute outliers with capping
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4190.0000000 4190.0000000
## na         50.0000000   50.0000000
## mean      236.6995227  235.8004773
## sd         44.5912839   41.5255437
## se_mean     0.6888790    0.6415172
## IQR        57.0000000   57.0000000
## skewness    0.8718806    0.2357734
## kurtosis    4.1298894   -0.3682497
## p00       107.0000000  124.0000000
## p01       153.0000000  154.0000000
## p05       170.0000000  170.0000000
## p10       183.0000000  183.0000000
## p20       200.0000000  200.0000000
## p25       206.0000000  206.0000000
## p30       212.0000000  212.0000000
## p40       223.0000000  223.0000000
## p50       234.0000000  234.0000000
## p60       244.0000000  244.0000000
## p70       257.0000000  257.0000000
## p75       263.0000000  263.0000000
## p80       271.0000000  271.0000000
## p90       292.0000000  292.0000000
## p95       312.0000000  312.0000000
## p99       354.1100000  334.1100000
## p100      696.0000000  347.0000000
plot(totChol1)

data2 <- cbind(data2,totChol1)

data2$totChol1 <- as.numeric(data2$totChol1)

data2 <- data2[,c(-8)]


totChol2 <- imputate_na(data2,totChol1,method = "mice",seed=100,print_flag = FALSE)

summary(totChol2)
## Warning: `cols` is now required.
## Please use `cols = c(statistic)`
## * Impute missing values based on Multivariate Imputation by Chained Equations
##  - method : mice
##  - random seed : 100
## 
## * Information of Imputation (before vs after)
##              Original   Imputation
## n        4190.0000000 4240.0000000
## na         50.0000000    0.0000000
## mean      235.8004773  235.7934434
## sd         41.5255437   41.3286665
## se_mean     0.6415172    0.6347000
## IQR        57.0000000   56.5500000
## skewness    0.2357734    0.2364781
## kurtosis   -0.3682497   -0.3477068
## p00       124.0000000  124.0000000
## p01       154.0000000  154.0000000
## p05       170.0000000  170.0000000
## p10       183.0000000  184.0000000
## p20       200.0000000  200.0000000
## p25       206.0000000  206.0000000
## p30       212.0000000  212.0000000
## p40       223.0000000  223.0000000
## p50       234.0000000  234.0000000
## p60       244.0000000  244.0000000
## p70       257.0000000  256.3000000
## p75       263.0000000  262.5500000
## p80       271.0000000  271.0000000
## p90       292.0000000  292.0000000
## p95       312.0000000  312.0000000
## p99       334.1100000  334.0000000
## p100      347.0000000  347.0000000
plot(totChol2)

data2 <- cbind(data2,totChol2)

data2$totChol2 <- as.numeric(data2$totChol2)

data2 <- data2[,c(-16)]


data2<-dplyr::rename(data2,"totChol"="totChol2")

boxplot(data2$totChol)

#############################################################



###################BPMeds#############################################

data2$BPMeds <- as.factor(data2$BPMeds)


BPMeds1 <- imputate_na(data2,BPMeds,method = "mode",print_flag = FALSE)#seed = 100)

summary(BPMeds1)
## Impute missing values with mode
## 
## * Information of Imputation (before vs after)
##      original imputation original_percent imputation_percent
## 0        4063       4116            95.83              97.08
## 1         124        124             2.92               2.92
## <NA>       53          0             1.25               0.00
plot(BPMeds1)

data2 <- cbind(data2,BPMeds1)

data2$BPMeds1 <- as.factor(data2$BPMeds1)

data2 <- data2[,c(-4)]


data2<-dplyr::rename(data2,"BPMeds"="BPMeds1")


###############################################################

#####Log transformation on dataset
# find_skewness(data2,index = FALSE, value = TRUE)
# 
# #sysBP, cigsPerDay, glucose are > 0.3 (threshold), hence will be transformed
# 
# sysBPLog <- transform(data2$sysBP, method = "log")
# 
# summary(sysBPLog)
# 
# plot(sysBPLog)
# 
# cigsPerDayLog <- transform(data2$cigsPerDay, method = "log")
# 
# summary(cigsPerDayLog)
# 
# #We find -Inf values, hence do log+1
# cigsPerDayLog <- transform(data2$cigsPerDay, method = "log+1")
# 
# summary(cigsPerDayLog)
# 
# plot(cigsPerDayLog)
# 
# glucoseLog <- transform(data2$glucose, method = "log")
# 
# summary(glucoseLog)
# 
# plot(glucoseLog)
# 
# sysBPLog <- as.numeric(sysBPLog)
# cigsPerDayLog <- as.numeric(cigsPerDayLog)
# glucoseLog <- as.numeric(glucoseLog)
# 
# endtime <- Sys.time()
# 
# print(endtime-starttime)
#  
# trandata2 <- cbind(data2,sysBPLog,glucoseLog,cigsPerDayLog)
# 
# find_skewness(trandata2,index = FALSE, value = TRUE)
# # Since the skewness has reduced, we will now remove the original vars of glucose, cigsperday, sysBP
# trandata2 <- trandata2[,c(-8,-11,-14)]

data2 <- data2[,c(1:6,8:16,7)]

write.csv(data2,file="C:/BigData/BABI/Capstone/data2.csv",col.names=TRUE)
## Warning in write.csv(data2, file = "C:/BigData/BABI/Capstone/data2.csv", :
## attempt to set 'col.names' ignored
  1. Binning(6)

binage <- cut(data2$age,breaks=c(30,40,50,60,70),labels=c("30t40","40t50","50t60","60t70"))
summary(binage)
## 30t40 40t50 50t60 60t70 
##   748  1609  1304   579
plot(binage)

newdata2 <- cbind(data2,binage)

# newdata2 <- newdata2[,-2]



#Ranges as defined by medical terms <18 - Underweight, 18-24.9 - Normal, 24.9-29.9-Overweight,>30 Obese)
binBMI <- cut(data2$BMI,breaks=c(0,18.5,24.9,29.9,Inf),labels=c("Underweight","Normal","Overweight","Obese"))
summary(binBMI)
## Underweight      Normal  Overweight       Obese 
##          56        1844        1785         555
plot(binBMI)

newdata2 <- cbind(newdata2,binBMI)

newdata2$TenYearCHD <- as.factor(newdata2$TenYearCHD)

newdata2 <- newdata2[,c(1:15,17:18,16)]
# 
# newdata2 <- newdata2[,-9]
# newdata2 <- newdata2[,c(1:5,7:16,6)]

#Binning for transformed dataset

# binageT <- cut(trandata2$age,breaks=c(30,40,50,60,70),labels=c("30t40","40t50","50t60","60t70"))
# summary(binageT)
# plot(binageT)
# 
# trandata2 <- cbind(trandata2,binageT)
# 
# trandata2 <- trandata2[,-2]
# 
# 
# 
# binBMIT <- cut(trandata2$BMI,breaks=c(0,18.5,24.9,29.9,Inf),labels=c("Underweight","Normal","Overweight","Obese"))
# summary(binBMIT)
# plot(binBMIT)
# 
# trandata2 <- cbind(trandata2,binBMIT)
# 
# trandata2 <- trandata2[,-8]
# trandata2 <- trandata2[,c(1:5,7:16,6)]

#write.csv(newdata2,file="C:/BigData/BABI/Capstone/Coronory Heart Risk Study/newdata.csv",col.names = TRUE,row.names = TRUE)

#write.csv(trandata2,file="C:/BigData/BABI/Capstone/Coronory Heart Risk Study/trandata2.csv",col.names = TRUE,row.names = TRUE)
dmnewdata<- dummy_cols(newdata2, select_columns = c("male","prevStroke","education","diabetes","prevHyp","prevStroke","BPMeds","binage","binBMI"))

dmnewdata<- dummy_cols(newdata2, select_columns = c("male","prevStroke","education","diabetes","prevHyp","prevStroke","BPMeds"))
#dmnewdata <- dmnewdata[,c(-1,-2,-3,-4,-5,-7,-13,-15,-14)]
#dmnewdata <- dmnewdata[,c(1:6,8:29,7)]

dmnewdata <- dmnewdata[,c(-1,-3,-4,-5,-6,-8,-15)]
dmnewdata <- dmnewdata[,c(1:8,10:23,9)]

#dmnewdata1 <- dummy_cols(newdata2,select_columns = c("education","binage","binBMI"))
#dmnewdata1 <- dmnewdata1[,c(-2,-7,-14,-15)]
#dmnewdata1 <- dmnewdata1[,c(1:11,13:24,12)]

Logistic Regression(11)

set.seed(123)


sample3 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

trainLr <- newdata2[sample3, ]
testLr  <- newdata2[-sample3,]



logitmod3 <- glm(TenYearCHD ~ ., family = "binomial", data=trainLr)

pred3L <- predict(logitmod3, newdata = testLr, type = "response")


y_pred_num3<- ifelse(pred3L > 0.5, 1, 0)
y_pred3 <- factor(y_pred_num3, levels=c(0, 1))
y_act3 <- factor(testLr$TenYearCHD)

caret::confusionMatrix(y_pred3,y_act3, positive="1", mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction    0    1
##          0 1066  178
##          1   12   15
##                                           
##                Accuracy : 0.8505          
##                  95% CI : (0.8297, 0.8697)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 0.426           
##                                           
##                   Kappa : 0.1029          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.07772         
##             Specificity : 0.98887         
##          Pos Pred Value : 0.55556         
##          Neg Pred Value : 0.85691         
##               Precision : 0.55556         
##                  Recall : 0.07772         
##                      F1 : 0.13636         
##              Prevalence : 0.15185         
##          Detection Rate : 0.01180         
##    Detection Prevalence : 0.02124         
##       Balanced Accuracy : 0.53329         
##                                           
##        'Positive' Class : 1               
## 
InformationValue::plotROC(y_act3,c(as.numeric(y_pred3)))

summary(logitmod3)
## 
## Call:
## glm(formula = TenYearCHD ~ ., family = "binomial", data = trainLr)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.3721  -0.6018  -0.4259  -0.2783   3.0322  
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)      -8.305334   1.369839  -6.063 1.34e-09 ***
## male1             0.618149   0.125060   4.943 7.70e-07 ***
## age               0.065568   0.020890   3.139 0.001697 ** 
## curSmoker1       -0.213682   0.185169  -1.154 0.248507    
## prevStroke1       0.622278   0.528791   1.177 0.239278    
## prevHyp1          0.184967   0.162913   1.135 0.256218    
## diabetes1         0.909291   0.265944   3.419 0.000628 ***
## glucose           0.001917   0.004660   0.411 0.680745    
## education2       -0.162058   0.137341  -1.180 0.238014    
## education3       -0.130386   0.167789  -0.777 0.437110    
## education4        0.099000   0.174260   0.568 0.569954    
## BMI              -0.004933   0.037578  -0.131 0.895566    
## sysBP             0.019610   0.004928   3.979 6.92e-05 ***
## diaBP            -0.002644   0.007844  -0.337 0.736092    
## heartRate        -0.003863   0.005002  -0.772 0.439889    
## cigsPerDay        0.028152   0.007528   3.740 0.000184 ***
## totChol           0.002390   0.001372   1.742 0.081480 .  
## BPMeds1           0.265891   0.268707   0.990 0.322409    
## binage40t50       0.125874   0.268105   0.469 0.638716    
## binage50t60       0.077465   0.416135   0.186 0.852323    
## binage60t70      -0.215688   0.568053  -0.380 0.704170    
## binBMINormal      0.001740   0.591494   0.003 0.997652    
## binBMIOverweight -0.177787   0.662442  -0.268 0.788406    
## binBMIObese      -0.154027   0.783439  -0.197 0.844137    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2529.6  on 2968  degrees of freedom
## Residual deviance: 2240.2  on 2945  degrees of freedom
## AIC: 2288.2
## 
## Number of Fisher Scoring iterations: 5
anova(logitmod3,test="Chisq")
## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: TenYearCHD
## 
## Terms added sequentially (first to last)
## 
## 
##            Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                        2968     2529.6              
## male        1   30.715      2967     2498.8 2.989e-08 ***
## age         1  142.636      2966     2356.2 < 2.2e-16 ***
## curSmoker   1    5.789      2965     2350.4 0.0161246 *  
## prevStroke  1    3.503      2964     2346.9 0.0612616 .  
## prevHyp     1   40.236      2963     2306.7 2.251e-10 ***
## diabetes    1   14.688      2962     2292.0 0.0001269 ***
## glucose     1    0.037      2961     2292.0 0.8466871    
## education   3    2.128      2958     2289.8 0.5463286    
## BMI         1    0.259      2957     2289.6 0.6104785    
## sysBP       1   25.675      2956     2263.9 4.039e-07 ***
## diaBP       1    0.092      2955     2263.8 0.7618733    
## heartRate   1    0.218      2954     2263.6 0.6406291    
## cigsPerDay  1   14.668      2953     2248.9 0.0001282 ***
## totChol     1    3.645      2952     2245.3 0.0562352 .  
## BPMeds      1    0.972      2951     2244.3 0.3240916    
## binage      3    3.027      2948     2241.3 0.3874297    
## binBMI      3    1.120      2945     2240.2 0.7722576    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef(logitmod3))
##      (Intercept)            male1              age       curSmoker1 
##     0.0002471947     1.8554907347     1.0677657533     0.8076051233 
##      prevStroke1         prevHyp1        diabetes1          glucose 
##     1.8631683216     1.2031791522     2.4825613748     1.0019193293 
##       education2       education3       education4              BMI 
##     0.8503917749     0.8777569046     1.1040667860     0.9950795185 
##            sysBP            diaBP        heartRate       cigsPerDay 
##     1.0198030501     0.9973598446     0.9961442698     1.0285518022 
##          totChol          BPMeds1      binage40t50      binage50t60 
##     1.0023927015     1.3045925944     1.1341390961     1.0805449423 
##      binage60t70     binBMINormal binBMIOverweight      binBMIObese 
##     0.8059867035     1.0017419924     0.8371206678     0.8572486011
exp(coef(logitmod3))/(1+exp(coef(logitmod3)))
##      (Intercept)            male1              age       curSmoker1 
##     0.0002471336     0.6497974979     0.5163862259     0.4467818291 
##      prevStroke1         prevHyp1        diabetes1          glucose 
##     0.6507365660     0.5461104473     0.7128550247     0.5004793723 
##       education2       education3       education4              BMI 
##     0.4595739056     0.4674497015     0.5247299151     0.4987668458 
##            sysBP            diaBP        heartRate       cigsPerDay 
##     0.5049022230     0.4993390887     0.4990342055     0.5070374841 
##          totChol          BPMeds1      binage40t50      binage50t60 
##     0.5005974606     0.5660838265     0.5314269806     0.5193566937 
##      binage60t70     binBMINormal binBMIOverweight      binBMIObese 
##     0.4462860673     0.5004351191     0.4556699418     0.4615691193
PseudoR2(logitmod3,c("McFadden", "Nagel"))
##   McFadden Nagelkerke 
##  0.1144105  0.1619653
lrtest(logitmod3)
## Likelihood ratio test
## 
## Model 1: TenYearCHD ~ male + age + curSmoker + prevStroke + prevHyp + 
##     diabetes + glucose + education + BMI + sysBP + diaBP + heartRate + 
##     cigsPerDay + totChol + BPMeds + binage + binBMI
## Model 2: TenYearCHD ~ 1
##   #Df  LogLik  Df  Chisq Pr(>Chisq)    
## 1  24 -1120.1                          
## 2   1 -1264.8 -23 289.41  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

NaiveBayes(12)

set.seed(1234)

samplen <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

trainNb <- newdata2[samplen, ]
testNb  <- newdata2[-samplen,]

#nbmod<-naiveBayes(x=trainN[,1:23], y=trainN[,24])
nbmod<-naiveBayes(x=trainNb[,1:17], y=trainNb[,18])
pred_nb<-predict(nbmod,newdata = testNb[,1:17])

table(pred_nb,testNb[,18])
##        
## pred_nb   0   1
##       0 924 120
##       1 154  73
caret::confusionMatrix(pred_nb,testNb$TenYearCHD,positive="1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 924 120
##          1 154  73
##                                           
##                Accuracy : 0.7844          
##                  95% CI : (0.7608, 0.8067)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1.0000          
##                                           
##                   Kappa : 0.2195          
##                                           
##  Mcnemar's Test P-Value : 0.0462          
##                                           
##             Sensitivity : 0.37824         
##             Specificity : 0.85714         
##          Pos Pred Value : 0.32159         
##          Neg Pred Value : 0.88506         
##               Precision : 0.32159         
##                  Recall : 0.37824         
##                      F1 : 0.34762         
##              Prevalence : 0.15185         
##          Detection Rate : 0.05744         
##    Detection Prevalence : 0.17860         
##       Balanced Accuracy : 0.61769         
##                                           
##        'Positive' Class : 1               
## 
InformationValue::plotROC(testNb$TenYearCHD,c(as.numeric(pred_nb)),Show.labels = T)
## Warning: Removed 101 rows containing missing values (geom_text).

pred_nb1<-predict(nbmod,newdata = trainNb[,1:17])
caret::confusionMatrix(pred_nb1,trainNb$TenYearCHD,positive="1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction    0    1
##          0 2143  265
##          1  375  186
##                                           
##                Accuracy : 0.7844          
##                  95% CI : (0.7692, 0.7991)
##     No Information Rate : 0.8481          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.2395          
##                                           
##  Mcnemar's Test P-Value : 1.643e-05       
##                                           
##             Sensitivity : 0.41242         
##             Specificity : 0.85107         
##          Pos Pred Value : 0.33155         
##          Neg Pred Value : 0.88995         
##               Precision : 0.33155         
##                  Recall : 0.41242         
##                      F1 : 0.36759         
##              Prevalence : 0.15190         
##          Detection Rate : 0.06265         
##    Detection Prevalence : 0.18895         
##       Balanced Accuracy : 0.63174         
##                                           
##        'Positive' Class : 1               
## 

SVM Classification(14)

set.seed(123)


sample3 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

trainSv <- newdata2[sample3, ]
testSv  <- newdata2[-sample3,]

starttime <- Sys.time()
svmModel3 <- e1071::svm(TenYearCHD~.,data = trainSv,kernel="polynomial",scale=FALSE,gamma=0.1,coef0=1)

predsvm <- predict(svmModel3,newdata = testSv)
endtime <- Sys.time()
print(endtime-starttime)
## Time difference of 4.534689 mins
caret::confusionMatrix(predsvm,testSv$TenYearCHD,positive="1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction    0    1
##          0 1023  177
##          1   55   16
##                                           
##                Accuracy : 0.8175          
##                  95% CI : (0.7951, 0.8383)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 0.9987          
##                                           
##                   Kappa : 0.0431          
##                                           
##  Mcnemar's Test P-Value : 1.957e-15       
##                                           
##             Sensitivity : 0.08290         
##             Specificity : 0.94898         
##          Pos Pred Value : 0.22535         
##          Neg Pred Value : 0.85250         
##               Precision : 0.22535         
##                  Recall : 0.08290         
##                      F1 : 0.12121         
##              Prevalence : 0.15185         
##          Detection Rate : 0.01259         
##    Detection Prevalence : 0.05586         
##       Balanced Accuracy : 0.51594         
##                                           
##        'Positive' Class : 1               
## 
####Tuning#####
#tuned_param <- tune.svm(TenYearCHD~.,data=train2S,gamma = 10^(-5:-1), cost = 10^(-3:1))

xgbTree from caret package (15)

set.seed(101)

#dmnewdata2 <- dummy_columns(newdata2,select_columns = c("education","BPMeds","prevStroke","prevHyp","diabetes","binageD","binBMID"))

#dmnewdata2 <- dmnewdata2[,c(-3,-6,-7,-8,-9,-17,-18)]
boostdata1 <- dmnewdata

sample3 <- createDataPartition(boostdata1$TenYearCHD,p=0.7,list = FALSE)

trainXG <- boostdata1[sample3, ]
testXG  <- boostdata1[-sample3,]


levels(trainXG$TenYearCHD) <- make.names(levels(trainXG$TenYearCHD))
levels(testXG$TenYearCHD) <- make.names(levels(testXG$TenYearCHD))


trnoutput_vector <- trainXG[,"TenYearCHD"]
tesoutput_vector <- testXG[,"TenYearCHD"]

xgb_trcontrol <- trainControl(
  method = "repeatedcv",
  number = 10, 
  repeats = 3,
  #allowParallel = TRUE,
  verboseIter = FALSE,
  #returnData = FALSE,
  summaryFunction = twoClassSummary,
  classProbs = TRUE,
  savePredictions=TRUE,
  sampling = "smote"
)

# xgbGrid <- expand.grid(nrounds = c(25,50,75),
#                         max_depth = 4:7,
#                          colsample_bytree = c(0.3,0.4,0.5),
#                          eta = c(0.05,0.1,0.3),
#                          gamma=0,
#                          min_child_weight = c(2.0,2.25),
#                          subsample = 1
#                         )
 xgbGrid <- expand.grid(nrounds = 50,
                        max_depth = 4,
                        colsample_bytree = 0.3,
                        eta = 0.05,
                        gamma=0,
                        min_child_weight = 2,
                        subsample = 1
                       )
#set.seed(0) 
#reset.seed()
#numberofcores = detectCores()  # review what number of cores does for your environment

#cl <- makeCluster(numberofcores, type = "SOCK")
# Register cluster so that caret will know to train in parallel.
#registerDoSNOW(cl)

starttime <- Sys.time()

xgb_model <- caret::train(TenYearCHD~.,data=trainXG,trControl = xgb_trcontrol,tuneGrid = xgbGrid,method = "xgbTree",preProcess = c("center","scale"))
## Warning in train.default(x, y, weights = w, ...): The metric "Accuracy" was
## not in the result set. ROC will be used instead.
#stopCluster(cl)

predicted <- predict(xgb_model, testXG)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 25.66165 secs
caret::confusionMatrix(predicted,tesoutput_vector, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 923 114
##         X1 155  79
##                                           
##                Accuracy : 0.7884          
##                  95% CI : (0.7649, 0.8105)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1.00000         
##                                           
##                   Kappa : 0.2442          
##                                           
##  Mcnemar's Test P-Value : 0.01473         
##                                           
##             Sensitivity : 0.40933         
##             Specificity : 0.85622         
##          Pos Pred Value : 0.33761         
##          Neg Pred Value : 0.89007         
##               Precision : 0.33761         
##                  Recall : 0.40933         
##                      F1 : 0.37002         
##              Prevalence : 0.15185         
##          Detection Rate : 0.06216         
##    Detection Prevalence : 0.18411         
##       Balanced Accuracy : 0.63277         
##                                           
##        'Positive' Class : X1              
## 
#xgb_model$finalModel
#xgb.plot.tree(model = xgb_model)

importanceC <- xgb.importance(feature_names = colnames(xgb_model$finalModel$feature_names), model = xgb_model$finalModel)

xgb.ggplot.importance(importanceC)

caret::varImp(xgb_model,useModel=TRUE,scale=FALSE)
## xgbTree variable importance
## 
##   only 20 most important variables shown (out of 26)
## 
##              Overall
## age         0.171159
## sysBP       0.160456
## cigsPerDay  0.106260
## education_1 0.080496
## heartRate   0.063157
## prevHyp_1   0.052085
## binage50t60 0.046288
## totChol     0.041807
## BMI         0.041790
## male_1      0.036981
## glucose     0.034336
## male_0      0.030788
## binage60t70 0.026763
## binage40t50 0.026182
## diaBP       0.020069
## prevHyp_0   0.016404
## diabetes_1  0.013742
## education_3 0.009618
## diabetes_0  0.009027
## education_2 0.005769
plot(caret::varImp(xgb_model,useModel=TRUE,scale=FALSE))

#xgb.plot.multi.trees(feature_names = names(xgb_model$finalModel$feature_names),model = xgb_model$finalModel)

#xgb.plot.tree(feature_names = xgb_model$finalModel$feature_names, model = xgb_model$finalModel)

xgbres <- evalm(xgb_model)
## ***MLeval: Machine Learning Model Evaluation in R***
## Input: caret train function object
## Averaging probs.
## Group 1 type: repeatedcv
## Observations: 2969
## Number of groups: 1
## Observations per group: 2969
## Positive: X1
## Negative: X0
## Group: Group 1
## Positive: 451
## Negative: 2518
## ***Performance Metrics***

## Group 1 Optimal Informedness = 0.302663395613666
## Group 1 AUC-ROC = 0.69

predicted1 <- predict(xgb_model, trainXG)

caret::confusionMatrix(predicted1,trnoutput_vector, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 2143  239
##         X1  375  212
##                                           
##                Accuracy : 0.7932          
##                  95% CI : (0.7782, 0.8076)
##     No Information Rate : 0.8481          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.2858          
##                                           
##  Mcnemar's Test P-Value : 5.089e-08       
##                                           
##             Sensitivity : 0.4701          
##             Specificity : 0.8511          
##          Pos Pred Value : 0.3612          
##          Neg Pred Value : 0.8997          
##               Precision : 0.3612          
##                  Recall : 0.4701          
##                      F1 : 0.4085          
##              Prevalence : 0.1519          
##          Detection Rate : 0.0714          
##    Detection Prevalence : 0.1977          
##       Balanced Accuracy : 0.6606          
##                                           
##        'Positive' Class : X1              
## 

LogitBoost Caret(16)

set.seed(108)
sample2 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

train2L <- newdata2[sample2, ]
test2L  <- newdata2[-sample2,]

levels(train2L$TenYearCHD) <- make.names(levels(train2L$TenYearCHD))
levels(test2L$TenYearCHD) <- make.names(levels(test2L$TenYearCHD))

repeats <- 5
numbers <- 10
tunel <- 10

x <- trainControl(method = "repeatedcv",
                 number = numbers,
                 repeats = repeats,
                 classProbs = TRUE,
                 summaryFunction = twoClassSummary,
                 sampling = "smote",
                 verboseIter = FALSE,
                 savePredictions = TRUE
                 )
starttime <- Sys.time()

lmmodel <- caret::train(TenYearCHD~., data = train2L, method = "LogitBoost",
               #preProcess = c("center","scale"),
               trControl = x,
               metric = "ROC",
               tuneLength = tunel)
# Summary of model

lgbmpredict <- predict(lmmodel,newdata = test2L)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 1.136121 mins
plot(lmmodel)

plot(lmmodel, print.thres = 0.5, type="S")

caret::varImp(lmmodel,scale=F)
## ROC curve variable importance
## 
##            Importance
## age            0.6893
## binage         0.6712
## sysBP          0.6456
## prevHyp        0.6066
## diaBP          0.5903
## totChol        0.5736
## male           0.5708
## glucose        0.5597
## BMI            0.5571
## binBMI         0.5519
## cigsPerDay     0.5360
## education      0.5350
## diabetes       0.5222
## BPMeds         0.5186
## curSmoker      0.5185
## heartRate      0.5154
## prevStroke     0.5038
plot(caret::varImp(lmmodel,scale=F))

caret::confusionMatrix(lgbmpredict,test2L$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 888 133
##         X1 190  60
##                                          
##                Accuracy : 0.7459         
##                  95% CI : (0.721, 0.7696)
##     No Information Rate : 0.8482         
##     P-Value [Acc > NIR] : 1.000000       
##                                          
##                   Kappa : 0.1201         
##                                          
##  Mcnemar's Test P-Value : 0.001834       
##                                          
##             Sensitivity : 0.31088        
##             Specificity : 0.82375        
##          Pos Pred Value : 0.24000        
##          Neg Pred Value : 0.86974        
##               Precision : 0.24000        
##                  Recall : 0.31088        
##                      F1 : 0.27088        
##              Prevalence : 0.15185        
##          Detection Rate : 0.04721        
##    Detection Prevalence : 0.19670        
##       Balanced Accuracy : 0.56731        
##                                          
##        'Positive' Class : X1             
## 
lgbmpredict1 <- predict(lmmodel,newdata = train2L)

caret::confusionMatrix(lgbmpredict1,train2L$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 2129  294
##         X1  389  157
##                                          
##                Accuracy : 0.77           
##                  95% CI : (0.7544, 0.785)
##     No Information Rate : 0.8481         
##     P-Value [Acc > NIR] : 1.0000000      
##                                          
##                   Kappa : 0.1782         
##                                          
##  Mcnemar's Test P-Value : 0.0003221      
##                                          
##             Sensitivity : 0.34812        
##             Specificity : 0.84551        
##          Pos Pred Value : 0.28755        
##          Neg Pred Value : 0.87866        
##               Precision : 0.28755        
##                  Recall : 0.34812        
##                      F1 : 0.31494        
##              Prevalence : 0.15190        
##          Detection Rate : 0.05288        
##    Detection Prevalence : 0.18390        
##       Balanced Accuracy : 0.59681        
##                                          
##        'Positive' Class : X1             
## 
lres <- evalm(lmmodel)
## ***MLeval: Machine Learning Model Evaluation in R***
## Input: caret train function object
## Averaging probs.
## Group 1 type: repeatedcv
## Observations: 2969
## Number of groups: 1
## Observations per group: 2969
## Positive: X1
## Negative: X0
## Group: Group 1
## Positive: 451
## Negative: 2518
## ***Performance Metrics***

## Group 1 Optimal Informedness = 0.256928826418743
## Group 1 AUC-ROC = 0.66

NB Caret(17)

set.seed(108)
sample2 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

train2N <- newdata2[sample2, ]
test2N  <- newdata2[-sample2,]

levels(train2N$TenYearCHD) <- make.names(levels(train2N$TenYearCHD))
levels(test2N$TenYearCHD) <- make.names(levels(test2N$TenYearCHD))

repeats <- 3
numbers <- 10
tunel <- 10

x <- trainControl(method = "repeatedcv",
                 number = numbers,
                 repeats = repeats,
                 classProbs = TRUE,
                 summaryFunction = twoClassSummary,
                 sampling = "smote",
                 verboseIter = FALSE,
                 savePredictions = TRUE
                 )
starttime <- Sys.time()

nbcmodel <- caret::train(TenYearCHD~., data = train2N, method = "naive_bayes",
               #
               trControl = x,
               metric = "ROC",
               tuneLength = tunel)
# Summary of model

nbcpredict <- predict(nbcmodel,newdata = test2N)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 25.79849 secs
plot(nbcmodel, print.thres = 0.5, type="S")

plot(nbcmodel)

imp <- caret::varImp(nbcmodel,useModel=TRUE,scale=FALSE)

plot(imp)

caret::confusionMatrix(nbcpredict,test2N$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 815  90
##         X1 263 103
##                                           
##                Accuracy : 0.7223          
##                  95% CI : (0.6968, 0.7467)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.2118          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.53368         
##             Specificity : 0.75603         
##          Pos Pred Value : 0.28142         
##          Neg Pred Value : 0.90055         
##               Precision : 0.28142         
##                  Recall : 0.53368         
##                      F1 : 0.36852         
##              Prevalence : 0.15185         
##          Detection Rate : 0.08104         
##    Detection Prevalence : 0.28796         
##       Balanced Accuracy : 0.64485         
##                                           
##        'Positive' Class : X1              
## 
res <- evalm(nbcmodel)
## ***MLeval: Machine Learning Model Evaluation in R***
## Input: caret train function object
## Averaging probs.
## Group 1 type: repeatedcv
## Observations: 2969
## Number of groups: 1
## Observations per group: 2969
## Positive: X1
## Negative: X0
## Group: Group 1
## Positive: 451
## Negative: 2518
## ***Performance Metrics***

## Group 1 Optimal Informedness = 0.321926915564917
## Group 1 AUC-ROC = 0.7

nbovpredict <- predict(nbcmodel,train2N)

caret::confusionMatrix(nbovpredict,train2N$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 1928  222
##         X1  590  229
##                                           
##                Accuracy : 0.7265          
##                  95% CI : (0.7101, 0.7425)
##     No Information Rate : 0.8481          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.2048          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.50776         
##             Specificity : 0.76569         
##          Pos Pred Value : 0.27961         
##          Neg Pred Value : 0.89674         
##               Precision : 0.27961         
##                  Recall : 0.50776         
##                      F1 : 0.36063         
##              Prevalence : 0.15190         
##          Detection Rate : 0.07713         
##    Detection Prevalence : 0.27585         
##       Balanced Accuracy : 0.63672         
##                                           
##        'Positive' Class : X1              
## 

Random Forest in caret(18)

set.seed(108)
sample2 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

train2R <- newdata2[sample2, ]
test2R  <- newdata2[-sample2,]

levels(train2R$TenYearCHD) <- make.names(levels(train2R$TenYearCHD))
levels(test2R$TenYearCHD) <- make.names(levels(test2R$TenYearCHD))

repeats <- 3
numbers <- 10
tunel <- 10

x <- trainControl(method = "repeatedcv",
                 number = numbers,
                 repeats = repeats,
                 classProbs = TRUE,
                 summaryFunction = twoClassSummary,
                 sampling = "smote",
                 verboseIter = FALSE,
                 savePredictions = TRUE,
                 search = "random"
                 )
starttime <- Sys.time()
mtry <- sqrt(ncol(newdata2))
tunegrid <- expand.grid(.mtry = mtry)
rfmodel <- caret::train(TenYearCHD~., data = train2R, method = "rf",
               #
               trControl = x,
               metric = "ROC",
               tuneLength = tunel,tunegrid=tunegrid,ntree=15)
# Summary of model

rfpredict <- predict(rfmodel,newdata = test2R)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 2.134187 mins
plot(rfmodel, print.thres = 0.5, type="S")

plot(rfmodel)

rfimp <- caret::varImp(rfmodel,useModel=TRUE,scale=FALSE)
plot(rfimp)

# Get the row names of the variable importance data
rownames(rfimp$importance)
##  [1] "male1"            "age"              "curSmoker1"      
##  [4] "prevStroke1"      "prevHyp1"         "diabetes1"       
##  [7] "glucose"          "education2"       "education3"      
## [10] "education4"       "BMI"              "sysBP"           
## [13] "diaBP"            "heartRate"        "cigsPerDay"      
## [16] "totChol"          "BPMeds1"          "binage40t50"     
## [19] "binage50t60"      "binage60t70"      "binBMINormal"    
## [22] "binBMIOverweight" "binBMIObese"
# Convert the variable importance data into a dataframe
importance <- data.frame(rownames(rfimp$importance), rfimp$importance$Overall)
# Relabel the data
names(importance)<-c('CHD', 'Importance')
# Order the data from greatest importance to least important
#importance <- transform(importance, CHD = reorder(CHD, Importance))
# Plot the data with ggplot.
ggplot(data=importance, aes(x=CHD, y=Importance)) +
  geom_bar(stat = 'identity',colour = "blue", fill = "white") + coord_flip()

#varImpPlot(rfmodel,n.var = min(10,nrow(rfmodel$importance)),scale = TRUE,main="Top 10 Variable of importance",sort=TRUE)
# plot(rfmodel$finalModel)
# legend("topright", c("OOB", "0", "1"), text.col=1:6, lty=1:3, col=1:3) 
# title(main="Error Rates Random Forest for CHD")
#tree_num <- rfmodel$finalModel$forest$ndbigtree
caret::confusionMatrix(rfpredict,test2R$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 909 116
##         X1 169  77
##                                           
##                Accuracy : 0.7758          
##                  95% CI : (0.7518, 0.7984)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1.000000        
##                                           
##                   Kappa : 0.2177          
##                                           
##  Mcnemar's Test P-Value : 0.002069        
##                                           
##             Sensitivity : 0.39896         
##             Specificity : 0.84323         
##          Pos Pred Value : 0.31301         
##          Neg Pred Value : 0.88683         
##               Precision : 0.31301         
##                  Recall : 0.39896         
##                      F1 : 0.35080         
##              Prevalence : 0.15185         
##          Detection Rate : 0.06058         
##    Detection Prevalence : 0.19355         
##       Balanced Accuracy : 0.62110         
##                                           
##        'Positive' Class : X1              
## 
rfpredict1 <- predict(rfmodel,newdata = train2R)

caret::confusionMatrix(rfpredict1,train2R$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 2200  221
##         X1  318  230
##                                           
##                Accuracy : 0.8185          
##                  95% CI : (0.8041, 0.8322)
##     No Information Rate : 0.8481          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.3526          
##                                           
##  Mcnemar's Test P-Value : 3.549e-05       
##                                           
##             Sensitivity : 0.50998         
##             Specificity : 0.87371         
##          Pos Pred Value : 0.41971         
##          Neg Pred Value : 0.90872         
##               Precision : 0.41971         
##                  Recall : 0.50998         
##                      F1 : 0.46046         
##              Prevalence : 0.15190         
##          Detection Rate : 0.07747         
##    Detection Prevalence : 0.18457         
##       Balanced Accuracy : 0.69184         
##                                           
##        'Positive' Class : X1              
## 
# tree_func <- function(final_model, 
#                       tree_num) {
#   
#   # get tree by index
#   tree <- randomForest::getTree(final_model, 
#                                 k = tree_num, 
#                                 labelVar = TRUE) %>%
#     tibble::rownames_to_column() %>%
#     # make leaf split points to NA, so the 0s won't get plotted
#     mutate(`split point` = ifelse(is.na(prediction), `split point`, NA))
#   
#   # prepare data frame for graph
#   graph_frame <- data.frame(from = rep(tree$rowname, 2),
#                             to = c(tree$`left daughter`, tree$`right daughter`))
#   
#   # convert to graph and delete the last node that we don't want to plot
#   graph <- graph_from_data_frame(graph_frame) %>%
#     delete_vertices("0")
#   
#   # set node labels
#   V(graph)$node_label <- gsub("_", " ", as.character(tree$`split var`))
#   V(graph)$leaf_label <- as.character(tree$prediction)
#   V(graph)$split <- as.character(round(tree$`split point`, digits = 2))
#   
#   # plot
#   plot <- ggraph(graph, 'dendogram') + 
#     theme_bw() +
#     geom_edge_link() +
#     geom_node_point() +
#     geom_node_text(aes(label = node_label), na.rm = TRUE, repel = TRUE) +
#     geom_node_label(aes(label = split), vjust = 2.5, na.rm = TRUE, fill = "white") +
#     geom_node_label(aes(label = leaf_label, fill = leaf_label), na.rm = TRUE, 
#                   repel = TRUE, colour = "white", fontface = "bold", show.legend = FALSE) +
#     theme(panel.grid.minor = element_blank(),
#           panel.grid.major = element_blank(),
#           panel.background = element_blank(),
#           plot.background = element_rect(fill = "white"),
#           panel.border = element_blank(),
#           axis.line = element_blank(),
#           axis.text.x = element_blank(),
#           axis.text.y = element_blank(),
#           axis.ticks = element_blank(),
#           axis.title.x = element_blank(),
#           axis.title.y = element_blank(),
#           plot.title = element_text(size = 18))
#   
#   print(plot)
# }
# tree_func(rfmodel$finalModel,tree_num)

rfres <- evalm(rfmodel)
## ***MLeval: Machine Learning Model Evaluation in R***
## Input: caret train function object
## Averaging probs.
## Group 1 type: repeatedcv
## Observations: 2969
## Number of groups: 1
## Observations per group: 2969
## Positive: X1
## Negative: X0
## Group: Group 1
## Positive: 451
## Negative: 2518
## ***Performance Metrics***

## Group 1 Optimal Informedness = 0.30398514289136
## Group 1 AUC-ROC = 0.7

CTREE(19)

set.seed(108)
sample2 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

train2CT <- newdata2[sample2, ]
test2CT  <- newdata2[-sample2,]

levels(train2CT$TenYearCHD) <- make.names(levels(train2CT$TenYearCHD))
levels(test2CT$TenYearCHD) <- make.names(levels(test2CT$TenYearCHD))

repeats <- 3
numbers <- 10
tunel <- 10

x <- trainControl(method = "repeatedcv",
                 number = numbers,
                 repeats = repeats,
                 classProbs = TRUE,
                 summaryFunction = twoClassSummary,
                 sampling = "smote",
                 verboseIter = FALSE,
                 savePredictions = TRUE
                 )
starttime <- Sys.time()
#mtry <- sqrt(ncol(newdata2))
#tgrid <- expand.grid(.mtry = mtry)
ctrmodel <- caret::train(TenYearCHD~., data = train2CT, method = "ctree",
               #
               trControl = x,
               metric = "ROC",
               tuneLength = tunel)
               #tunegrid=tgrid,
               #ntree=5)
# Summary of model

ctpredict <- predict(ctrmodel,newdata = test2CT)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 2.714082 mins
plot(ctrmodel, print.thres = 0.5, type="S")

imp <- caret::varImp(ctrmodel,useModel=TRUE,scale=FALSE)

plot(imp)

plot(ctrmodel$finalModel)

#plot(as.simpleparty(ctrmodel$finalModel))
caret::confusionMatrix(ctpredict,test2CT$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 811  95
##         X1 267  98
##                                           
##                Accuracy : 0.7152          
##                  95% CI : (0.6895, 0.7399)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.1904          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.5078          
##             Specificity : 0.7523          
##          Pos Pred Value : 0.2685          
##          Neg Pred Value : 0.8951          
##               Precision : 0.2685          
##                  Recall : 0.5078          
##                      F1 : 0.3513          
##              Prevalence : 0.1518          
##          Detection Rate : 0.0771          
##    Detection Prevalence : 0.2872          
##       Balanced Accuracy : 0.6300          
##                                           
##        'Positive' Class : X1              
## 
ctpredict1 <- predict(ctrmodel,newdata = train2CT)

caret::confusionMatrix(ctpredict1,train2CT$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 1945  188
##         X1  573  263
##                                           
##                Accuracy : 0.7437          
##                  95% CI : (0.7276, 0.7593)
##     No Information Rate : 0.8481          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.2633          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.58315         
##             Specificity : 0.77244         
##          Pos Pred Value : 0.31459         
##          Neg Pred Value : 0.91186         
##               Precision : 0.31459         
##                  Recall : 0.58315         
##                      F1 : 0.40870         
##              Prevalence : 0.15190         
##          Detection Rate : 0.08858         
##    Detection Prevalence : 0.28158         
##       Balanced Accuracy : 0.67779         
##                                           
##        'Positive' Class : X1              
## 
ctres <- evalm(ctrmodel)
## ***MLeval: Machine Learning Model Evaluation in R***
## Input: caret train function object
## Averaging probs.
## Group 1 type: repeatedcv
## Observations: 2969
## Number of groups: 1
## Observations per group: 2969
## Positive: X1
## Negative: X0
## Group: Group 1
## Positive: 451
## Negative: 2518
## ***Performance Metrics***

## Group 1 Optimal Informedness = 0.324576574164904
## Group 1 AUC-ROC = 0.7

ctres$roc

C45(20)

set.seed(108)
sample2 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

train2C5 <- newdata2[sample2, ]
test2C5  <- newdata2[-sample2,]

levels(train2C5$TenYearCHD) <- make.names(levels(train2C5$TenYearCHD))
levels(test2C5$TenYearCHD) <- make.names(levels(test2C5$TenYearCHD))

repeats <- 3
numbers <- 10
tunel <- 10

x <- trainControl(method = "repeatedcv",
                 number = numbers,
                 repeats = repeats,
                 classProbs = TRUE,
                 summaryFunction = twoClassSummary,
                 sampling = "smote",
                 verboseIter = FALSE,
                 savePredictions = TRUE
                 )
starttime <- Sys.time()
#mtry <- sqrt(ncol(newdata2))
#tgrid <- expand.grid(maxdepth = 25)
c5model <- caret::train(TenYearCHD~., data = train2C5, method = "rpart2",
               #
               trControl = x,
               metric = "ROC",
               tuneLength = tunel)
## note: only 4 possible values of the max tree depth from the initial fit.
##  Truncating the grid to 4 .
               #tunegrid=tgrid)
               #ntree=5)
# Summary of model

c5predict <- predict(c5model,newdata = test2C5)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 15.81627 secs
#plot(c5model, print.thres = 0.5, type="S")

imp <- caret::varImp(c5model,useModel=TRUE,scale=FALSE)

plot(imp)

fancyRpartPlot(c5model$finalModel,palettes=c("Blues","Oranges"))

caret::confusionMatrix(c5predict,test2C5$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 830 103
##         X1 248  90
##                                           
##                Accuracy : 0.7238          
##                  95% CI : (0.6984, 0.7483)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.1806          
##                                           
##  Mcnemar's Test P-Value : 1.516e-14       
##                                           
##             Sensitivity : 0.46632         
##             Specificity : 0.76994         
##          Pos Pred Value : 0.26627         
##          Neg Pred Value : 0.88960         
##               Precision : 0.26627         
##                  Recall : 0.46632         
##                      F1 : 0.33898         
##              Prevalence : 0.15185         
##          Detection Rate : 0.07081         
##    Detection Prevalence : 0.26593         
##       Balanced Accuracy : 0.61813         
##                                           
##        'Positive' Class : X1              
## 
c5predict1 <- predict(c5model,newdata = train2C5)

caret::confusionMatrix(c5predict1,train2C5$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 1963  195
##         X1  555  256
##                                           
##                Accuracy : 0.7474          
##                  95% CI : (0.7314, 0.7629)
##     No Information Rate : 0.8481          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.2615          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.56763         
##             Specificity : 0.77959         
##          Pos Pred Value : 0.31566         
##          Neg Pred Value : 0.90964         
##               Precision : 0.31566         
##                  Recall : 0.56763         
##                      F1 : 0.40571         
##              Prevalence : 0.15190         
##          Detection Rate : 0.08622         
##    Detection Prevalence : 0.27316         
##       Balanced Accuracy : 0.67361         
##                                           
##        'Positive' Class : X1              
## 
c45res <- evalm(c5model)
## ***MLeval: Machine Learning Model Evaluation in R***
## Input: caret train function object
## Averaging probs.
## Group 1 type: repeatedcv
## Observations: 2969
## Number of groups: 1
## Observations per group: 2969
## Positive: X1
## Negative: X0
## Group: Group 1
## Positive: 451
## Negative: 2518
## ***Performance Metrics***

## Group 1 Optimal Informedness = 0.334238273785727
## Group 1 AUC-ROC = 0.7

KNN(21)

set.seed(101)

sample3 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

trainKS <- newdata2[sample3, ]
testKS  <- newdata2[-sample3,]


# Setting levels for both training and validation data
levels(trainKS$TenYearCHD) <- make.names(levels(trainKS$TenYearCHD))
levels(testKS$TenYearCHD) <- make.names(levels(testKS$TenYearCHD))


repeats <- 3
numbers <- 10
tunel <- 10

x <- trainControl(method = "repeatedcv",
                 number = numbers,
                 repeats = repeats,
                 classProbs = TRUE,
                 summaryFunction = twoClassSummary,
                 sampling = "smote",
                 verboseIter = FALSE,
                 savePredictions = TRUE
                 )
starttime <- Sys.time()
modelK2 <- caret::train(TenYearCHD~., data = trainKS, method = "knn",
               preProcess = c("center","scale"),
               trControl = x,
               metric = "ROC",
               tuneLength = tunel)

# Summary of model

knnpredict <- predict(modelK2,newdata = testKS)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 2.670699 mins
#plot(modelK2)

plot(modelK2, print.thres = 0.5, type="S")

knnImp <- caret::varImp(modelK2,useModel=TRUE,scale=FALSE)


plot(knnImp)

caret::confusionMatrix(knnpredict,testKS$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 744  83
##         X1 334 110
##                                           
##                Accuracy : 0.6719          
##                  95% CI : (0.6453, 0.6977)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.1696          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.56995         
##             Specificity : 0.69017         
##          Pos Pred Value : 0.24775         
##          Neg Pred Value : 0.89964         
##               Precision : 0.24775         
##                  Recall : 0.56995         
##                      F1 : 0.34537         
##              Prevalence : 0.15185         
##          Detection Rate : 0.08655         
##    Detection Prevalence : 0.34933         
##       Balanced Accuracy : 0.63006         
##                                           
##        'Positive' Class : X1              
## 
knnpredict1 <- predict(modelK2,trainKS)
caret::confusionMatrix(knnpredict1,trainKS$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 1772  147
##         X1  746  304
##                                           
##                Accuracy : 0.6992          
##                  95% CI : (0.6824, 0.7157)
##     No Information Rate : 0.8481          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.2445          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.6741          
##             Specificity : 0.7037          
##          Pos Pred Value : 0.2895          
##          Neg Pred Value : 0.9234          
##               Precision : 0.2895          
##                  Recall : 0.6741          
##                      F1 : 0.4051          
##              Prevalence : 0.1519          
##          Detection Rate : 0.1024          
##    Detection Prevalence : 0.3537          
##       Balanced Accuracy : 0.6889          
##                                           
##        'Positive' Class : X1              
## 
knres <- evalm(modelK2)
## ***MLeval: Machine Learning Model Evaluation in R***
## Input: caret train function object
## Averaging probs.
## Group 1 type: repeatedcv
## Observations: 2969
## Number of groups: 1
## Observations per group: 2969
## Positive: X1
## Negative: X0
## Group: Group 1
## Positive: 451
## Negative: 2518
## ***Performance Metrics***

## Group 1 Optimal Informedness = 0.234290932338163
## Group 1 AUC-ROC = 0.65

GBM(22)

set.seed(101)


sample2 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

train2GB <- newdata2[sample2, ]
test2GB  <- newdata2[-sample2,]

# Setting levels for both training and validation data
levels(train2GB$TenYearCHD) <- make.names(levels(train2GB$TenYearCHD))
levels(test2GB$TenYearCHD) <- make.names(levels(test2GB$TenYearCHD))

#repeats <- 3
numbers <- 10
tunel <- 10

x <- trainControl(method = "cv",
                 number = numbers,
                 #repeats = repeats,
                 classProbs = TRUE,
                 summaryFunction = twoClassSummary,
                 sampling = "smote",
                 verboseIter = TRUE
                 #savePredictions = TRUE
                 )
starttime <- Sys.time()
gbmodel <- caret::train(TenYearCHD~., data = train2GB, method = "gbm",
               preProcess = c("center","scale"),
               trControl = x,
               metric = "ROC",
               tuneLength = tunel)
## + Fold01: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3495             nan     0.1000    0.0078
##      2        1.3331             nan     0.1000    0.0064
##      3        1.3216             nan     0.1000    0.0051
##      4        1.3110             nan     0.1000    0.0049
##      5        1.3001             nan     0.1000    0.0046
##      6        1.2915             nan     0.1000    0.0039
##      7        1.2842             nan     0.1000    0.0035
##      8        1.2777             nan     0.1000    0.0024
##      9        1.2693             nan     0.1000    0.0033
##     10        1.2637             nan     0.1000    0.0026
##     20        1.2212             nan     0.1000    0.0011
##     40        1.1761             nan     0.1000    0.0003
##     60        1.1491             nan     0.1000    0.0006
##     80        1.1289             nan     0.1000    0.0001
##    100        1.1118             nan     0.1000   -0.0003
##    120        1.0976             nan     0.1000    0.0000
##    140        1.0855             nan     0.1000   -0.0002
##    160        1.0754             nan     0.1000   -0.0002
##    180        1.0662             nan     0.1000   -0.0000
##    200        1.0577             nan     0.1000   -0.0003
##    220        1.0504             nan     0.1000   -0.0002
##    240        1.0424             nan     0.1000   -0.0001
##    260        1.0352             nan     0.1000   -0.0001
##    280        1.0283             nan     0.1000   -0.0004
##    300        1.0224             nan     0.1000    0.0001
##    320        1.0161             nan     0.1000   -0.0001
##    340        1.0095             nan     0.1000   -0.0000
##    360        1.0027             nan     0.1000   -0.0001
##    380        0.9981             nan     0.1000   -0.0001
##    400        0.9928             nan     0.1000   -0.0003
##    420        0.9874             nan     0.1000   -0.0002
##    440        0.9836             nan     0.1000   -0.0002
##    460        0.9786             nan     0.1000   -0.0002
##    480        0.9733             nan     0.1000   -0.0000
##    500        0.9684             nan     0.1000    0.0000
## 
## - Fold01: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3441             nan     0.1000    0.0105
##      2        1.3233             nan     0.1000    0.0093
##      3        1.3075             nan     0.1000    0.0073
##      4        1.2917             nan     0.1000    0.0064
##      5        1.2798             nan     0.1000    0.0056
##      6        1.2683             nan     0.1000    0.0048
##      7        1.2595             nan     0.1000    0.0037
##      8        1.2503             nan     0.1000    0.0030
##      9        1.2420             nan     0.1000    0.0041
##     10        1.2344             nan     0.1000    0.0033
##     20        1.1815             nan     0.1000    0.0005
##     40        1.1147             nan     0.1000    0.0003
##     60        1.0626             nan     0.1000    0.0011
##     80        1.0235             nan     0.1000    0.0001
##    100        0.9968             nan     0.1000    0.0002
##    120        0.9653             nan     0.1000   -0.0006
##    140        0.9410             nan     0.1000    0.0001
##    160        0.9224             nan     0.1000    0.0005
##    180        0.9003             nan     0.1000    0.0002
##    200        0.8837             nan     0.1000   -0.0004
##    220        0.8695             nan     0.1000   -0.0001
##    240        0.8545             nan     0.1000   -0.0003
##    260        0.8381             nan     0.1000   -0.0002
##    280        0.8235             nan     0.1000   -0.0001
##    300        0.8100             nan     0.1000   -0.0003
##    320        0.7977             nan     0.1000   -0.0002
##    340        0.7849             nan     0.1000   -0.0001
##    360        0.7740             nan     0.1000   -0.0001
##    380        0.7646             nan     0.1000   -0.0001
##    400        0.7550             nan     0.1000   -0.0002
##    420        0.7472             nan     0.1000   -0.0005
##    440        0.7394             nan     0.1000   -0.0001
##    460        0.7327             nan     0.1000   -0.0003
##    480        0.7228             nan     0.1000   -0.0003
##    500        0.7123             nan     0.1000   -0.0006
## 
## - Fold01: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3380             nan     0.1000    0.0138
##      2        1.3116             nan     0.1000    0.0122
##      3        1.2903             nan     0.1000    0.0104
##      4        1.2734             nan     0.1000    0.0082
##      5        1.2579             nan     0.1000    0.0067
##      6        1.2453             nan     0.1000    0.0057
##      7        1.2325             nan     0.1000    0.0053
##      8        1.2218             nan     0.1000    0.0044
##      9        1.2100             nan     0.1000    0.0054
##     10        1.2007             nan     0.1000    0.0038
##     20        1.1314             nan     0.1000    0.0018
##     40        1.0328             nan     0.1000    0.0005
##     60        0.9772             nan     0.1000    0.0007
##     80        0.9351             nan     0.1000    0.0005
##    100        0.8998             nan     0.1000    0.0005
##    120        0.8593             nan     0.1000    0.0008
##    140        0.8313             nan     0.1000    0.0001
##    160        0.8089             nan     0.1000    0.0002
##    180        0.7873             nan     0.1000   -0.0000
##    200        0.7664             nan     0.1000    0.0003
##    220        0.7492             nan     0.1000    0.0003
##    240        0.7306             nan     0.1000   -0.0001
##    260        0.7135             nan     0.1000   -0.0003
##    280        0.6965             nan     0.1000    0.0004
##    300        0.6813             nan     0.1000   -0.0004
##    320        0.6665             nan     0.1000    0.0004
##    340        0.6542             nan     0.1000   -0.0001
##    360        0.6416             nan     0.1000   -0.0001
##    380        0.6300             nan     0.1000   -0.0002
##    400        0.6180             nan     0.1000   -0.0004
##    420        0.6064             nan     0.1000   -0.0003
##    440        0.5954             nan     0.1000   -0.0001
##    460        0.5853             nan     0.1000   -0.0002
##    480        0.5751             nan     0.1000   -0.0000
##    500        0.5658             nan     0.1000   -0.0003
## 
## - Fold01: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3377             nan     0.1000    0.0143
##      2        1.3162             nan     0.1000    0.0096
##      3        1.2953             nan     0.1000    0.0094
##      4        1.2776             nan     0.1000    0.0076
##      5        1.2587             nan     0.1000    0.0089
##      6        1.2434             nan     0.1000    0.0064
##      7        1.2292             nan     0.1000    0.0060
##      8        1.2185             nan     0.1000    0.0044
##      9        1.2060             nan     0.1000    0.0048
##     10        1.1972             nan     0.1000    0.0036
##     20        1.1107             nan     0.1000    0.0038
##     40        1.0144             nan     0.1000    0.0001
##     60        0.9394             nan     0.1000    0.0005
##     80        0.8899             nan     0.1000   -0.0002
##    100        0.8533             nan     0.1000   -0.0000
##    120        0.8135             nan     0.1000    0.0007
##    140        0.7786             nan     0.1000   -0.0000
##    160        0.7458             nan     0.1000    0.0002
##    180        0.7211             nan     0.1000   -0.0004
##    200        0.6975             nan     0.1000   -0.0003
##    220        0.6740             nan     0.1000   -0.0004
##    240        0.6522             nan     0.1000   -0.0001
##    260        0.6312             nan     0.1000   -0.0001
##    280        0.6134             nan     0.1000   -0.0001
##    300        0.5964             nan     0.1000   -0.0006
##    320        0.5798             nan     0.1000    0.0001
##    340        0.5630             nan     0.1000   -0.0001
##    360        0.5484             nan     0.1000   -0.0004
##    380        0.5355             nan     0.1000   -0.0003
##    400        0.5221             nan     0.1000   -0.0003
##    420        0.5087             nan     0.1000   -0.0002
##    440        0.4971             nan     0.1000   -0.0002
##    460        0.4862             nan     0.1000   -0.0003
##    480        0.4738             nan     0.1000   -0.0001
##    500        0.4629             nan     0.1000   -0.0003
## 
## - Fold01: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3296             nan     0.1000    0.0160
##      2        1.3005             nan     0.1000    0.0135
##      3        1.2757             nan     0.1000    0.0117
##      4        1.2525             nan     0.1000    0.0098
##      5        1.2321             nan     0.1000    0.0090
##      6        1.2157             nan     0.1000    0.0071
##      7        1.1982             nan     0.1000    0.0073
##      8        1.1848             nan     0.1000    0.0051
##      9        1.1720             nan     0.1000    0.0050
##     10        1.1610             nan     0.1000    0.0047
##     20        1.0702             nan     0.1000    0.0026
##     40        0.9570             nan     0.1000    0.0008
##     60        0.8897             nan     0.1000    0.0005
##     80        0.8274             nan     0.1000    0.0007
##    100        0.7833             nan     0.1000    0.0000
##    120        0.7424             nan     0.1000    0.0001
##    140        0.7084             nan     0.1000    0.0000
##    160        0.6797             nan     0.1000   -0.0003
##    180        0.6533             nan     0.1000    0.0006
##    200        0.6281             nan     0.1000    0.0002
##    220        0.6031             nan     0.1000    0.0001
##    240        0.5830             nan     0.1000   -0.0005
##    260        0.5593             nan     0.1000   -0.0002
##    280        0.5377             nan     0.1000   -0.0004
##    300        0.5215             nan     0.1000   -0.0002
##    320        0.5052             nan     0.1000   -0.0005
##    340        0.4877             nan     0.1000   -0.0004
##    360        0.4730             nan     0.1000   -0.0007
##    380        0.4591             nan     0.1000   -0.0002
##    400        0.4452             nan     0.1000   -0.0003
##    420        0.4305             nan     0.1000   -0.0003
##    440        0.4177             nan     0.1000   -0.0004
##    460        0.4056             nan     0.1000    0.0002
##    480        0.3946             nan     0.1000   -0.0000
##    500        0.3830             nan     0.1000   -0.0003
## 
## - Fold01: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3332             nan     0.1000    0.0162
##      2        1.3053             nan     0.1000    0.0133
##      3        1.2797             nan     0.1000    0.0111
##      4        1.2566             nan     0.1000    0.0097
##      5        1.2380             nan     0.1000    0.0079
##      6        1.2210             nan     0.1000    0.0070
##      7        1.2023             nan     0.1000    0.0089
##      8        1.1891             nan     0.1000    0.0051
##      9        1.1748             nan     0.1000    0.0058
##     10        1.1570             nan     0.1000    0.0069
##     20        1.0579             nan     0.1000    0.0026
##     40        0.9298             nan     0.1000    0.0015
##     60        0.8563             nan     0.1000    0.0007
##     80        0.7940             nan     0.1000    0.0012
##    100        0.7510             nan     0.1000    0.0004
##    120        0.7038             nan     0.1000    0.0002
##    140        0.6645             nan     0.1000    0.0003
##    160        0.6306             nan     0.1000   -0.0001
##    180        0.6010             nan     0.1000   -0.0005
##    200        0.5704             nan     0.1000    0.0001
##    220        0.5476             nan     0.1000   -0.0003
##    240        0.5246             nan     0.1000   -0.0004
##    260        0.5036             nan     0.1000   -0.0002
##    280        0.4837             nan     0.1000   -0.0004
##    300        0.4661             nan     0.1000   -0.0005
##    320        0.4473             nan     0.1000    0.0001
##    340        0.4285             nan     0.1000   -0.0001
##    360        0.4127             nan     0.1000   -0.0008
##    380        0.3977             nan     0.1000   -0.0005
##    400        0.3826             nan     0.1000   -0.0002
##    420        0.3681             nan     0.1000   -0.0001
##    440        0.3555             nan     0.1000   -0.0003
##    460        0.3415             nan     0.1000   -0.0002
##    480        0.3289             nan     0.1000   -0.0001
##    500        0.3166             nan     0.1000   -0.0002
## 
## - Fold01: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3316             nan     0.1000    0.0161
##      2        1.2980             nan     0.1000    0.0142
##      3        1.2699             nan     0.1000    0.0116
##      4        1.2448             nan     0.1000    0.0126
##      5        1.2250             nan     0.1000    0.0081
##      6        1.2066             nan     0.1000    0.0075
##      7        1.1912             nan     0.1000    0.0067
##      8        1.1751             nan     0.1000    0.0070
##      9        1.1629             nan     0.1000    0.0043
##     10        1.1504             nan     0.1000    0.0046
##     20        1.0521             nan     0.1000    0.0017
##     40        0.9216             nan     0.1000    0.0000
##     60        0.8371             nan     0.1000    0.0003
##     80        0.7705             nan     0.1000    0.0003
##    100        0.7174             nan     0.1000   -0.0000
##    120        0.6724             nan     0.1000    0.0001
##    140        0.6310             nan     0.1000    0.0002
##    160        0.5945             nan     0.1000    0.0002
##    180        0.5599             nan     0.1000   -0.0003
##    200        0.5316             nan     0.1000   -0.0004
##    220        0.5092             nan     0.1000   -0.0001
##    240        0.4835             nan     0.1000   -0.0002
##    260        0.4621             nan     0.1000   -0.0007
##    280        0.4410             nan     0.1000   -0.0000
##    300        0.4197             nan     0.1000   -0.0005
##    320        0.3996             nan     0.1000   -0.0002
##    340        0.3812             nan     0.1000   -0.0005
##    360        0.3656             nan     0.1000   -0.0004
##    380        0.3506             nan     0.1000   -0.0002
##    400        0.3360             nan     0.1000   -0.0004
##    420        0.3229             nan     0.1000   -0.0004
##    440        0.3102             nan     0.1000   -0.0004
##    460        0.2984             nan     0.1000   -0.0003
##    480        0.2868             nan     0.1000   -0.0003
##    500        0.2758             nan     0.1000   -0.0003
## 
## - Fold01: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3269             nan     0.1000    0.0176
##      2        1.2924             nan     0.1000    0.0164
##      3        1.2614             nan     0.1000    0.0133
##      4        1.2366             nan     0.1000    0.0108
##      5        1.2135             nan     0.1000    0.0100
##      6        1.1929             nan     0.1000    0.0087
##      7        1.1729             nan     0.1000    0.0082
##      8        1.1565             nan     0.1000    0.0055
##      9        1.1389             nan     0.1000    0.0074
##     10        1.1241             nan     0.1000    0.0066
##     20        1.0105             nan     0.1000    0.0030
##     40        0.8658             nan     0.1000    0.0011
##     60        0.7846             nan     0.1000   -0.0001
##     80        0.7142             nan     0.1000   -0.0001
##    100        0.6609             nan     0.1000   -0.0001
##    120        0.6136             nan     0.1000   -0.0000
##    140        0.5722             nan     0.1000    0.0000
##    160        0.5323             nan     0.1000    0.0000
##    180        0.4997             nan     0.1000   -0.0006
##    200        0.4727             nan     0.1000   -0.0004
##    220        0.4487             nan     0.1000   -0.0003
##    240        0.4247             nan     0.1000   -0.0002
##    260        0.4021             nan     0.1000   -0.0003
##    280        0.3809             nan     0.1000   -0.0002
##    300        0.3628             nan     0.1000   -0.0004
##    320        0.3422             nan     0.1000   -0.0003
##    340        0.3237             nan     0.1000   -0.0004
##    360        0.3080             nan     0.1000   -0.0002
##    380        0.2937             nan     0.1000   -0.0003
##    400        0.2794             nan     0.1000   -0.0004
##    420        0.2667             nan     0.1000   -0.0004
##    440        0.2529             nan     0.1000   -0.0002
##    460        0.2414             nan     0.1000   -0.0001
##    480        0.2305             nan     0.1000   -0.0001
##    500        0.2204             nan     0.1000   -0.0003
## 
## - Fold01: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3285             nan     0.1000    0.0149
##      2        1.2973             nan     0.1000    0.0128
##      3        1.2659             nan     0.1000    0.0143
##      4        1.2388             nan     0.1000    0.0111
##      5        1.2181             nan     0.1000    0.0080
##      6        1.1962             nan     0.1000    0.0078
##      7        1.1766             nan     0.1000    0.0088
##      8        1.1588             nan     0.1000    0.0067
##      9        1.1451             nan     0.1000    0.0050
##     10        1.1303             nan     0.1000    0.0055
##     20        1.0170             nan     0.1000    0.0015
##     40        0.8681             nan     0.1000    0.0014
##     60        0.7737             nan     0.1000    0.0011
##     80        0.7016             nan     0.1000   -0.0001
##    100        0.6460             nan     0.1000   -0.0005
##    120        0.5923             nan     0.1000    0.0004
##    140        0.5488             nan     0.1000   -0.0004
##    160        0.5099             nan     0.1000   -0.0001
##    180        0.4748             nan     0.1000   -0.0001
##    200        0.4452             nan     0.1000   -0.0004
##    220        0.4166             nan     0.1000   -0.0004
##    240        0.3924             nan     0.1000   -0.0004
##    260        0.3707             nan     0.1000    0.0001
##    280        0.3498             nan     0.1000   -0.0005
##    300        0.3286             nan     0.1000   -0.0001
##    320        0.3091             nan     0.1000   -0.0001
##    340        0.2924             nan     0.1000   -0.0002
##    360        0.2781             nan     0.1000   -0.0002
##    380        0.2629             nan     0.1000   -0.0003
##    400        0.2487             nan     0.1000   -0.0003
##    420        0.2354             nan     0.1000   -0.0003
##    440        0.2234             nan     0.1000   -0.0001
##    460        0.2116             nan     0.1000   -0.0001
##    480        0.2007             nan     0.1000   -0.0001
##    500        0.1893             nan     0.1000   -0.0001
## 
## - Fold01: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold01: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3267             nan     0.1000    0.0179
##      2        1.2942             nan     0.1000    0.0151
##      3        1.2679             nan     0.1000    0.0111
##      4        1.2440             nan     0.1000    0.0094
##      5        1.2169             nan     0.1000    0.0126
##      6        1.1959             nan     0.1000    0.0083
##      7        1.1763             nan     0.1000    0.0082
##      8        1.1550             nan     0.1000    0.0091
##      9        1.1350             nan     0.1000    0.0083
##     10        1.1199             nan     0.1000    0.0056
##     20        0.9960             nan     0.1000    0.0034
##     40        0.8368             nan     0.1000    0.0025
##     60        0.7353             nan     0.1000    0.0009
##     80        0.6635             nan     0.1000   -0.0001
##    100        0.6071             nan     0.1000    0.0002
##    120        0.5564             nan     0.1000   -0.0007
##    140        0.5153             nan     0.1000   -0.0005
##    160        0.4808             nan     0.1000   -0.0005
##    180        0.4452             nan     0.1000   -0.0005
##    200        0.4135             nan     0.1000   -0.0003
##    220        0.3853             nan     0.1000   -0.0003
##    240        0.3578             nan     0.1000    0.0002
##    260        0.3365             nan     0.1000   -0.0005
##    280        0.3137             nan     0.1000   -0.0003
##    300        0.2945             nan     0.1000   -0.0005
##    320        0.2756             nan     0.1000   -0.0002
##    340        0.2583             nan     0.1000   -0.0002
##    360        0.2435             nan     0.1000   -0.0002
##    380        0.2300             nan     0.1000   -0.0002
##    400        0.2173             nan     0.1000   -0.0002
##    420        0.2055             nan     0.1000   -0.0004
##    440        0.1937             nan     0.1000   -0.0001
##    460        0.1825             nan     0.1000   -0.0002
##    480        0.1721             nan     0.1000   -0.0003
##    500        0.1620             nan     0.1000   -0.0002
## 
## - Fold01: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3499             nan     0.1000    0.0080
##      2        1.3351             nan     0.1000    0.0067
##      3        1.3240             nan     0.1000    0.0057
##      4        1.3150             nan     0.1000    0.0036
##      5        1.3061             nan     0.1000    0.0045
##      6        1.2977             nan     0.1000    0.0042
##      7        1.2920             nan     0.1000    0.0025
##      8        1.2847             nan     0.1000    0.0037
##      9        1.2783             nan     0.1000    0.0027
##     10        1.2730             nan     0.1000    0.0025
##     20        1.2332             nan     0.1000    0.0012
##     40        1.1918             nan     0.1000    0.0004
##     60        1.1656             nan     0.1000   -0.0001
##     80        1.1467             nan     0.1000   -0.0001
##    100        1.1335             nan     0.1000    0.0002
##    120        1.1217             nan     0.1000   -0.0002
##    140        1.1112             nan     0.1000    0.0000
##    160        1.1023             nan     0.1000   -0.0001
##    180        1.0934             nan     0.1000   -0.0001
##    200        1.0848             nan     0.1000   -0.0001
##    220        1.0786             nan     0.1000   -0.0002
##    240        1.0715             nan     0.1000    0.0001
##    260        1.0647             nan     0.1000    0.0001
##    280        1.0583             nan     0.1000   -0.0001
##    300        1.0524             nan     0.1000   -0.0004
##    320        1.0460             nan     0.1000   -0.0001
##    340        1.0405             nan     0.1000   -0.0001
##    360        1.0347             nan     0.1000   -0.0002
##    380        1.0292             nan     0.1000   -0.0004
##    400        1.0242             nan     0.1000   -0.0003
##    420        1.0192             nan     0.1000   -0.0002
##    440        1.0143             nan     0.1000   -0.0003
##    460        1.0090             nan     0.1000   -0.0001
##    480        1.0043             nan     0.1000   -0.0003
##    500        0.9995             nan     0.1000   -0.0003
## 
## - Fold02: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3430             nan     0.1000    0.0115
##      2        1.3271             nan     0.1000    0.0079
##      3        1.3104             nan     0.1000    0.0069
##      4        1.2951             nan     0.1000    0.0072
##      5        1.2822             nan     0.1000    0.0061
##      6        1.2703             nan     0.1000    0.0052
##      7        1.2594             nan     0.1000    0.0044
##      8        1.2505             nan     0.1000    0.0037
##      9        1.2427             nan     0.1000    0.0037
##     10        1.2351             nan     0.1000    0.0034
##     20        1.1715             nan     0.1000    0.0028
##     40        1.1061             nan     0.1000    0.0001
##     60        1.0526             nan     0.1000    0.0014
##     80        1.0093             nan     0.1000    0.0015
##    100        0.9803             nan     0.1000    0.0011
##    120        0.9525             nan     0.1000   -0.0004
##    140        0.9335             nan     0.1000   -0.0001
##    160        0.9168             nan     0.1000    0.0000
##    180        0.9005             nan     0.1000   -0.0002
##    200        0.8793             nan     0.1000   -0.0003
##    220        0.8662             nan     0.1000   -0.0006
##    240        0.8468             nan     0.1000   -0.0002
##    260        0.8313             nan     0.1000   -0.0006
##    280        0.8158             nan     0.1000   -0.0002
##    300        0.8028             nan     0.1000    0.0002
##    320        0.7917             nan     0.1000    0.0002
##    340        0.7783             nan     0.1000   -0.0002
##    360        0.7700             nan     0.1000   -0.0002
##    380        0.7593             nan     0.1000   -0.0002
##    400        0.7503             nan     0.1000   -0.0005
##    420        0.7422             nan     0.1000   -0.0001
##    440        0.7366             nan     0.1000   -0.0002
##    460        0.7276             nan     0.1000   -0.0002
##    480        0.7189             nan     0.1000   -0.0001
##    500        0.7108             nan     0.1000   -0.0006
## 
## - Fold02: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3395             nan     0.1000    0.0123
##      2        1.3161             nan     0.1000    0.0102
##      3        1.2966             nan     0.1000    0.0081
##      4        1.2788             nan     0.1000    0.0088
##      5        1.2629             nan     0.1000    0.0072
##      6        1.2471             nan     0.1000    0.0069
##      7        1.2338             nan     0.1000    0.0057
##      8        1.2195             nan     0.1000    0.0072
##      9        1.2082             nan     0.1000    0.0048
##     10        1.1983             nan     0.1000    0.0044
##     20        1.1277             nan     0.1000    0.0015
##     40        1.0461             nan     0.1000    0.0010
##     60        0.9941             nan     0.1000    0.0001
##     80        0.9469             nan     0.1000    0.0006
##    100        0.9122             nan     0.1000   -0.0001
##    120        0.8771             nan     0.1000   -0.0002
##    140        0.8444             nan     0.1000   -0.0001
##    160        0.8155             nan     0.1000    0.0002
##    180        0.7909             nan     0.1000   -0.0004
##    200        0.7690             nan     0.1000    0.0008
##    220        0.7435             nan     0.1000   -0.0002
##    240        0.7252             nan     0.1000   -0.0001
##    260        0.7061             nan     0.1000   -0.0001
##    280        0.6896             nan     0.1000   -0.0001
##    300        0.6744             nan     0.1000   -0.0001
##    320        0.6614             nan     0.1000    0.0000
##    340        0.6450             nan     0.1000   -0.0004
##    360        0.6322             nan     0.1000   -0.0002
##    380        0.6188             nan     0.1000    0.0003
##    400        0.6062             nan     0.1000   -0.0005
##    420        0.5946             nan     0.1000   -0.0006
##    440        0.5842             nan     0.1000    0.0000
##    460        0.5741             nan     0.1000    0.0002
##    480        0.5621             nan     0.1000    0.0001
##    500        0.5509             nan     0.1000   -0.0001
## 
## - Fold02: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3400             nan     0.1000    0.0121
##      2        1.3153             nan     0.1000    0.0111
##      3        1.2939             nan     0.1000    0.0099
##      4        1.2777             nan     0.1000    0.0079
##      5        1.2605             nan     0.1000    0.0081
##      6        1.2457             nan     0.1000    0.0057
##      7        1.2339             nan     0.1000    0.0046
##      8        1.2187             nan     0.1000    0.0078
##      9        1.2065             nan     0.1000    0.0048
##     10        1.1962             nan     0.1000    0.0037
##     20        1.1213             nan     0.1000    0.0027
##     40        1.0229             nan     0.1000    0.0012
##     60        0.9545             nan     0.1000   -0.0000
##     80        0.8997             nan     0.1000   -0.0003
##    100        0.8535             nan     0.1000    0.0003
##    120        0.8122             nan     0.1000    0.0002
##    140        0.7782             nan     0.1000   -0.0000
##    160        0.7512             nan     0.1000   -0.0001
##    180        0.7248             nan     0.1000   -0.0003
##    200        0.6943             nan     0.1000   -0.0000
##    220        0.6706             nan     0.1000   -0.0004
##    240        0.6515             nan     0.1000   -0.0002
##    260        0.6300             nan     0.1000   -0.0006
##    280        0.6120             nan     0.1000   -0.0004
##    300        0.5938             nan     0.1000    0.0003
##    320        0.5764             nan     0.1000    0.0001
##    340        0.5605             nan     0.1000   -0.0002
##    360        0.5459             nan     0.1000   -0.0003
##    380        0.5323             nan     0.1000   -0.0002
##    400        0.5197             nan     0.1000   -0.0004
##    420        0.5057             nan     0.1000   -0.0001
##    440        0.4934             nan     0.1000   -0.0004
##    460        0.4840             nan     0.1000   -0.0001
##    480        0.4734             nan     0.1000   -0.0002
##    500        0.4624             nan     0.1000   -0.0004
## 
## - Fold02: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3368             nan     0.1000    0.0131
##      2        1.3115             nan     0.1000    0.0117
##      3        1.2915             nan     0.1000    0.0086
##      4        1.2695             nan     0.1000    0.0097
##      5        1.2526             nan     0.1000    0.0071
##      6        1.2363             nan     0.1000    0.0067
##      7        1.2222             nan     0.1000    0.0054
##      8        1.2095             nan     0.1000    0.0050
##      9        1.1971             nan     0.1000    0.0053
##     10        1.1867             nan     0.1000    0.0032
##     20        1.0996             nan     0.1000    0.0030
##     40        0.9935             nan     0.1000    0.0016
##     60        0.9205             nan     0.1000    0.0001
##     80        0.8552             nan     0.1000    0.0005
##    100        0.8070             nan     0.1000   -0.0002
##    120        0.7652             nan     0.1000    0.0008
##    140        0.7283             nan     0.1000    0.0004
##    160        0.6916             nan     0.1000    0.0004
##    180        0.6602             nan     0.1000   -0.0002
##    200        0.6307             nan     0.1000   -0.0004
##    220        0.6058             nan     0.1000    0.0001
##    240        0.5845             nan     0.1000   -0.0001
##    260        0.5627             nan     0.1000   -0.0003
##    280        0.5432             nan     0.1000   -0.0003
##    300        0.5273             nan     0.1000   -0.0004
##    320        0.5082             nan     0.1000   -0.0000
##    340        0.4903             nan     0.1000   -0.0001
##    360        0.4745             nan     0.1000   -0.0003
##    380        0.4580             nan     0.1000   -0.0003
##    400        0.4444             nan     0.1000   -0.0000
##    420        0.4310             nan     0.1000   -0.0002
##    440        0.4171             nan     0.1000   -0.0001
##    460        0.4046             nan     0.1000   -0.0002
##    480        0.3934             nan     0.1000   -0.0003
##    500        0.3816             nan     0.1000   -0.0001
## 
## - Fold02: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3318             nan     0.1000    0.0158
##      2        1.3055             nan     0.1000    0.0124
##      3        1.2800             nan     0.1000    0.0120
##      4        1.2608             nan     0.1000    0.0079
##      5        1.2426             nan     0.1000    0.0074
##      6        1.2219             nan     0.1000    0.0094
##      7        1.2071             nan     0.1000    0.0067
##      8        1.1940             nan     0.1000    0.0049
##      9        1.1812             nan     0.1000    0.0052
##     10        1.1689             nan     0.1000    0.0051
##     20        1.0767             nan     0.1000    0.0020
##     40        0.9411             nan     0.1000    0.0006
##     60        0.8567             nan     0.1000    0.0006
##     80        0.7925             nan     0.1000    0.0002
##    100        0.7444             nan     0.1000    0.0010
##    120        0.7014             nan     0.1000    0.0008
##    140        0.6624             nan     0.1000    0.0008
##    160        0.6311             nan     0.1000   -0.0003
##    180        0.6008             nan     0.1000    0.0000
##    200        0.5671             nan     0.1000    0.0002
##    220        0.5427             nan     0.1000   -0.0001
##    240        0.5191             nan     0.1000    0.0000
##    260        0.4969             nan     0.1000   -0.0000
##    280        0.4786             nan     0.1000   -0.0006
##    300        0.4576             nan     0.1000    0.0001
##    320        0.4390             nan     0.1000   -0.0003
##    340        0.4215             nan     0.1000   -0.0003
##    360        0.4056             nan     0.1000   -0.0004
##    380        0.3901             nan     0.1000   -0.0005
##    400        0.3734             nan     0.1000   -0.0003
##    420        0.3601             nan     0.1000   -0.0005
##    440        0.3456             nan     0.1000   -0.0002
##    460        0.3323             nan     0.1000   -0.0004
##    480        0.3195             nan     0.1000   -0.0002
##    500        0.3083             nan     0.1000   -0.0002
## 
## - Fold02: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3325             nan     0.1000    0.0147
##      2        1.2999             nan     0.1000    0.0137
##      3        1.2737             nan     0.1000    0.0110
##      4        1.2521             nan     0.1000    0.0099
##      5        1.2314             nan     0.1000    0.0090
##      6        1.2128             nan     0.1000    0.0069
##      7        1.1970             nan     0.1000    0.0064
##      8        1.1818             nan     0.1000    0.0066
##      9        1.1655             nan     0.1000    0.0066
##     10        1.1542             nan     0.1000    0.0045
##     20        1.0513             nan     0.1000    0.0016
##     40        0.9292             nan     0.1000    0.0015
##     60        0.8515             nan     0.1000    0.0001
##     80        0.7849             nan     0.1000    0.0014
##    100        0.7218             nan     0.1000    0.0001
##    120        0.6736             nan     0.1000    0.0000
##    140        0.6331             nan     0.1000    0.0003
##    160        0.5964             nan     0.1000   -0.0003
##    180        0.5655             nan     0.1000   -0.0003
##    200        0.5368             nan     0.1000   -0.0003
##    220        0.5063             nan     0.1000   -0.0003
##    240        0.4827             nan     0.1000   -0.0004
##    260        0.4610             nan     0.1000   -0.0002
##    280        0.4359             nan     0.1000    0.0002
##    300        0.4172             nan     0.1000   -0.0000
##    320        0.3972             nan     0.1000   -0.0003
##    340        0.3796             nan     0.1000   -0.0003
##    360        0.3625             nan     0.1000   -0.0001
##    380        0.3472             nan     0.1000    0.0000
##    400        0.3332             nan     0.1000   -0.0006
##    420        0.3183             nan     0.1000   -0.0002
##    440        0.3041             nan     0.1000   -0.0001
##    460        0.2916             nan     0.1000   -0.0004
##    480        0.2804             nan     0.1000   -0.0000
##    500        0.2688             nan     0.1000   -0.0002
## 
## - Fold02: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3306             nan     0.1000    0.0161
##      2        1.3010             nan     0.1000    0.0128
##      3        1.2780             nan     0.1000    0.0098
##      4        1.2557             nan     0.1000    0.0087
##      5        1.2349             nan     0.1000    0.0087
##      6        1.2171             nan     0.1000    0.0067
##      7        1.1991             nan     0.1000    0.0066
##      8        1.1820             nan     0.1000    0.0066
##      9        1.1650             nan     0.1000    0.0070
##     10        1.1518             nan     0.1000    0.0048
##     20        1.0392             nan     0.1000    0.0031
##     40        0.9021             nan     0.1000    0.0024
##     60        0.8123             nan     0.1000    0.0012
##     80        0.7459             nan     0.1000   -0.0001
##    100        0.6916             nan     0.1000   -0.0003
##    120        0.6444             nan     0.1000   -0.0000
##    140        0.5976             nan     0.1000   -0.0003
##    160        0.5566             nan     0.1000   -0.0003
##    180        0.5229             nan     0.1000    0.0001
##    200        0.4941             nan     0.1000    0.0001
##    220        0.4689             nan     0.1000   -0.0005
##    240        0.4433             nan     0.1000    0.0002
##    260        0.4202             nan     0.1000   -0.0004
##    280        0.3965             nan     0.1000   -0.0003
##    300        0.3751             nan     0.1000   -0.0003
##    320        0.3557             nan     0.1000   -0.0001
##    340        0.3375             nan     0.1000   -0.0003
##    360        0.3199             nan     0.1000   -0.0003
##    380        0.3048             nan     0.1000   -0.0004
##    400        0.2907             nan     0.1000   -0.0004
##    420        0.2770             nan     0.1000   -0.0004
##    440        0.2629             nan     0.1000   -0.0000
##    460        0.2507             nan     0.1000    0.0001
##    480        0.2400             nan     0.1000   -0.0002
##    500        0.2287             nan     0.1000   -0.0003
## 
## - Fold02: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3242             nan     0.1000    0.0196
##      2        1.2907             nan     0.1000    0.0156
##      3        1.2646             nan     0.1000    0.0119
##      4        1.2368             nan     0.1000    0.0117
##      5        1.2145             nan     0.1000    0.0089
##      6        1.1928             nan     0.1000    0.0085
##      7        1.1724             nan     0.1000    0.0073
##      8        1.1547             nan     0.1000    0.0069
##      9        1.1394             nan     0.1000    0.0060
##     10        1.1238             nan     0.1000    0.0062
##     20        1.0152             nan     0.1000    0.0018
##     40        0.8683             nan     0.1000    0.0002
##     60        0.7686             nan     0.1000    0.0016
##     80        0.6979             nan     0.1000    0.0008
##    100        0.6387             nan     0.1000    0.0010
##    120        0.5884             nan     0.1000    0.0003
##    140        0.5483             nan     0.1000   -0.0006
##    160        0.5081             nan     0.1000   -0.0009
##    180        0.4748             nan     0.1000   -0.0005
##    200        0.4406             nan     0.1000   -0.0001
##    220        0.4164             nan     0.1000   -0.0004
##    240        0.3905             nan     0.1000   -0.0002
##    260        0.3690             nan     0.1000    0.0001
##    280        0.3478             nan     0.1000    0.0000
##    300        0.3288             nan     0.1000   -0.0003
##    320        0.3102             nan     0.1000   -0.0000
##    340        0.2939             nan     0.1000   -0.0005
##    360        0.2769             nan     0.1000   -0.0002
##    380        0.2619             nan     0.1000   -0.0000
##    400        0.2478             nan     0.1000   -0.0001
##    420        0.2346             nan     0.1000   -0.0001
##    440        0.2227             nan     0.1000   -0.0001
##    460        0.2117             nan     0.1000   -0.0003
##    480        0.2005             nan     0.1000   -0.0001
##    500        0.1906             nan     0.1000   -0.0001
## 
## - Fold02: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold02: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3253             nan     0.1000    0.0196
##      2        1.2934             nan     0.1000    0.0131
##      3        1.2683             nan     0.1000    0.0100
##      4        1.2434             nan     0.1000    0.0098
##      5        1.2242             nan     0.1000    0.0079
##      6        1.2020             nan     0.1000    0.0097
##      7        1.1844             nan     0.1000    0.0061
##      8        1.1668             nan     0.1000    0.0061
##      9        1.1537             nan     0.1000    0.0039
##     10        1.1367             nan     0.1000    0.0072
##     20        1.0182             nan     0.1000    0.0026
##     40        0.8587             nan     0.1000    0.0013
##     60        0.7624             nan     0.1000    0.0008
##     80        0.6870             nan     0.1000    0.0003
##    100        0.6247             nan     0.1000   -0.0004
##    120        0.5741             nan     0.1000    0.0002
##    140        0.5272             nan     0.1000    0.0004
##    160        0.4874             nan     0.1000   -0.0008
##    180        0.4548             nan     0.1000   -0.0003
##    200        0.4237             nan     0.1000   -0.0006
##    220        0.3953             nan     0.1000   -0.0001
##    240        0.3702             nan     0.1000   -0.0003
##    260        0.3465             nan     0.1000   -0.0002
##    280        0.3265             nan     0.1000   -0.0005
##    300        0.3056             nan     0.1000   -0.0005
##    320        0.2860             nan     0.1000   -0.0004
##    340        0.2689             nan     0.1000   -0.0006
##    360        0.2539             nan     0.1000   -0.0001
##    380        0.2379             nan     0.1000   -0.0003
##    400        0.2255             nan     0.1000   -0.0001
##    420        0.2126             nan     0.1000   -0.0002
##    440        0.2011             nan     0.1000   -0.0002
##    460        0.1893             nan     0.1000   -0.0004
##    480        0.1792             nan     0.1000   -0.0002
##    500        0.1689             nan     0.1000   -0.0001
## 
## - Fold02: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3507             nan     0.1000    0.0071
##      2        1.3384             nan     0.1000    0.0058
##      3        1.3263             nan     0.1000    0.0056
##      4        1.3175             nan     0.1000    0.0045
##      5        1.3073             nan     0.1000    0.0043
##      6        1.2991             nan     0.1000    0.0040
##      7        1.2910             nan     0.1000    0.0034
##      8        1.2820             nan     0.1000    0.0037
##      9        1.2751             nan     0.1000    0.0033
##     10        1.2693             nan     0.1000    0.0026
##     20        1.2214             nan     0.1000    0.0017
##     40        1.1716             nan     0.1000    0.0005
##     60        1.1414             nan     0.1000    0.0002
##     80        1.1194             nan     0.1000    0.0000
##    100        1.1038             nan     0.1000    0.0000
##    120        1.0915             nan     0.1000    0.0002
##    140        1.0813             nan     0.1000   -0.0003
##    160        1.0728             nan     0.1000   -0.0004
##    180        1.0639             nan     0.1000   -0.0000
##    200        1.0560             nan     0.1000   -0.0003
##    220        1.0486             nan     0.1000   -0.0003
##    240        1.0408             nan     0.1000   -0.0001
##    260        1.0340             nan     0.1000    0.0000
##    280        1.0273             nan     0.1000   -0.0002
##    300        1.0191             nan     0.1000   -0.0000
##    320        1.0131             nan     0.1000   -0.0000
##    340        1.0079             nan     0.1000   -0.0004
##    360        1.0014             nan     0.1000   -0.0002
##    380        0.9964             nan     0.1000   -0.0004
##    400        0.9915             nan     0.1000   -0.0002
##    420        0.9854             nan     0.1000    0.0000
##    440        0.9804             nan     0.1000   -0.0003
##    460        0.9755             nan     0.1000   -0.0002
##    480        0.9713             nan     0.1000   -0.0002
##    500        0.9661             nan     0.1000   -0.0001
## 
## - Fold03: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3429             nan     0.1000    0.0107
##      2        1.3247             nan     0.1000    0.0086
##      3        1.3072             nan     0.1000    0.0076
##      4        1.2945             nan     0.1000    0.0061
##      5        1.2817             nan     0.1000    0.0057
##      6        1.2695             nan     0.1000    0.0052
##      7        1.2593             nan     0.1000    0.0037
##      8        1.2487             nan     0.1000    0.0038
##      9        1.2394             nan     0.1000    0.0040
##     10        1.2336             nan     0.1000    0.0021
##     20        1.1746             nan     0.1000    0.0018
##     40        1.1023             nan     0.1000    0.0007
##     60        1.0560             nan     0.1000    0.0002
##     80        1.0200             nan     0.1000    0.0002
##    100        0.9876             nan     0.1000    0.0001
##    120        0.9629             nan     0.1000   -0.0005
##    140        0.9404             nan     0.1000    0.0008
##    160        0.9174             nan     0.1000   -0.0004
##    180        0.8986             nan     0.1000    0.0000
##    200        0.8797             nan     0.1000    0.0000
##    220        0.8651             nan     0.1000   -0.0001
##    240        0.8497             nan     0.1000    0.0003
##    260        0.8362             nan     0.1000   -0.0005
##    280        0.8236             nan     0.1000   -0.0003
##    300        0.8058             nan     0.1000   -0.0003
##    320        0.7913             nan     0.1000   -0.0003
##    340        0.7792             nan     0.1000   -0.0003
##    360        0.7668             nan     0.1000    0.0002
##    380        0.7536             nan     0.1000   -0.0002
##    400        0.7443             nan     0.1000   -0.0003
##    420        0.7339             nan     0.1000   -0.0004
##    440        0.7266             nan     0.1000   -0.0002
##    460        0.7154             nan     0.1000   -0.0003
##    480        0.7051             nan     0.1000   -0.0002
##    500        0.6979             nan     0.1000   -0.0003
## 
## - Fold03: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3399             nan     0.1000    0.0134
##      2        1.3165             nan     0.1000    0.0097
##      3        1.2987             nan     0.1000    0.0074
##      4        1.2821             nan     0.1000    0.0076
##      5        1.2677             nan     0.1000    0.0067
##      6        1.2525             nan     0.1000    0.0069
##      7        1.2401             nan     0.1000    0.0055
##      8        1.2287             nan     0.1000    0.0040
##      9        1.2196             nan     0.1000    0.0030
##     10        1.2094             nan     0.1000    0.0035
##     20        1.1350             nan     0.1000    0.0016
##     40        1.0481             nan     0.1000    0.0012
##     60        0.9958             nan     0.1000    0.0001
##     80        0.9480             nan     0.1000    0.0005
##    100        0.9128             nan     0.1000    0.0015
##    120        0.8794             nan     0.1000   -0.0003
##    140        0.8469             nan     0.1000   -0.0001
##    160        0.8184             nan     0.1000    0.0000
##    180        0.7950             nan     0.1000    0.0002
##    200        0.7750             nan     0.1000    0.0000
##    220        0.7540             nan     0.1000   -0.0001
##    240        0.7376             nan     0.1000   -0.0002
##    260        0.7190             nan     0.1000   -0.0005
##    280        0.7034             nan     0.1000   -0.0000
##    300        0.6885             nan     0.1000    0.0001
##    320        0.6758             nan     0.1000   -0.0000
##    340        0.6629             nan     0.1000   -0.0001
##    360        0.6500             nan     0.1000    0.0001
##    380        0.6361             nan     0.1000    0.0004
##    400        0.6237             nan     0.1000   -0.0002
##    420        0.6129             nan     0.1000   -0.0003
##    440        0.6029             nan     0.1000   -0.0003
##    460        0.5911             nan     0.1000   -0.0006
##    480        0.5792             nan     0.1000   -0.0002
##    500        0.5705             nan     0.1000   -0.0003
## 
## - Fold03: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3358             nan     0.1000    0.0141
##      2        1.3113             nan     0.1000    0.0119
##      3        1.2917             nan     0.1000    0.0086
##      4        1.2728             nan     0.1000    0.0073
##      5        1.2575             nan     0.1000    0.0068
##      6        1.2423             nan     0.1000    0.0071
##      7        1.2300             nan     0.1000    0.0054
##      8        1.2190             nan     0.1000    0.0043
##      9        1.2077             nan     0.1000    0.0047
##     10        1.1968             nan     0.1000    0.0040
##     20        1.1161             nan     0.1000    0.0012
##     40        1.0159             nan     0.1000    0.0008
##     60        0.9481             nan     0.1000    0.0004
##     80        0.8995             nan     0.1000    0.0019
##    100        0.8639             nan     0.1000   -0.0001
##    120        0.8234             nan     0.1000   -0.0003
##    140        0.7929             nan     0.1000    0.0003
##    160        0.7636             nan     0.1000    0.0006
##    180        0.7362             nan     0.1000   -0.0001
##    200        0.7143             nan     0.1000   -0.0000
##    220        0.6914             nan     0.1000   -0.0005
##    240        0.6675             nan     0.1000   -0.0001
##    260        0.6498             nan     0.1000   -0.0004
##    280        0.6313             nan     0.1000   -0.0001
##    300        0.6135             nan     0.1000   -0.0000
##    320        0.6016             nan     0.1000   -0.0002
##    340        0.5861             nan     0.1000   -0.0003
##    360        0.5707             nan     0.1000    0.0002
##    380        0.5570             nan     0.1000   -0.0005
##    400        0.5426             nan     0.1000   -0.0004
##    420        0.5292             nan     0.1000   -0.0003
##    440        0.5166             nan     0.1000   -0.0005
##    460        0.5049             nan     0.1000   -0.0003
##    480        0.4930             nan     0.1000   -0.0005
##    500        0.4814             nan     0.1000   -0.0002
## 
## - Fold03: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3347             nan     0.1000    0.0138
##      2        1.3052             nan     0.1000    0.0130
##      3        1.2816             nan     0.1000    0.0108
##      4        1.2625             nan     0.1000    0.0091
##      5        1.2436             nan     0.1000    0.0078
##      6        1.2280             nan     0.1000    0.0065
##      7        1.2131             nan     0.1000    0.0057
##      8        1.1990             nan     0.1000    0.0058
##      9        1.1880             nan     0.1000    0.0047
##     10        1.1782             nan     0.1000    0.0042
##     20        1.0904             nan     0.1000    0.0021
##     40        0.9638             nan     0.1000    0.0024
##     60        0.8946             nan     0.1000    0.0017
##     80        0.8337             nan     0.1000    0.0010
##    100        0.7897             nan     0.1000   -0.0004
##    120        0.7529             nan     0.1000   -0.0001
##    140        0.7216             nan     0.1000    0.0001
##    160        0.6890             nan     0.1000   -0.0002
##    180        0.6600             nan     0.1000   -0.0002
##    200        0.6332             nan     0.1000    0.0003
##    220        0.6085             nan     0.1000   -0.0002
##    240        0.5861             nan     0.1000   -0.0002
##    260        0.5633             nan     0.1000   -0.0000
##    280        0.5429             nan     0.1000   -0.0001
##    300        0.5245             nan     0.1000    0.0001
##    320        0.5072             nan     0.1000   -0.0003
##    340        0.4895             nan     0.1000   -0.0004
##    360        0.4715             nan     0.1000    0.0001
##    380        0.4540             nan     0.1000   -0.0005
##    400        0.4393             nan     0.1000   -0.0003
##    420        0.4254             nan     0.1000   -0.0002
##    440        0.4110             nan     0.1000   -0.0002
##    460        0.4004             nan     0.1000   -0.0003
##    480        0.3886             nan     0.1000   -0.0001
##    500        0.3775             nan     0.1000   -0.0005
## 
## - Fold03: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3320             nan     0.1000    0.0154
##      2        1.3046             nan     0.1000    0.0121
##      3        1.2767             nan     0.1000    0.0123
##      4        1.2549             nan     0.1000    0.0107
##      5        1.2357             nan     0.1000    0.0085
##      6        1.2145             nan     0.1000    0.0093
##      7        1.1995             nan     0.1000    0.0055
##      8        1.1842             nan     0.1000    0.0057
##      9        1.1707             nan     0.1000    0.0044
##     10        1.1589             nan     0.1000    0.0039
##     20        1.0662             nan     0.1000    0.0040
##     40        0.9425             nan     0.1000    0.0009
##     60        0.8603             nan     0.1000    0.0008
##     80        0.7960             nan     0.1000   -0.0001
##    100        0.7468             nan     0.1000    0.0005
##    120        0.7019             nan     0.1000   -0.0001
##    140        0.6695             nan     0.1000    0.0000
##    160        0.6354             nan     0.1000    0.0001
##    180        0.6050             nan     0.1000   -0.0005
##    200        0.5765             nan     0.1000   -0.0007
##    220        0.5521             nan     0.1000   -0.0001
##    240        0.5288             nan     0.1000   -0.0006
##    260        0.5094             nan     0.1000   -0.0003
##    280        0.4904             nan     0.1000    0.0001
##    300        0.4713             nan     0.1000   -0.0006
##    320        0.4518             nan     0.1000   -0.0000
##    340        0.4341             nan     0.1000   -0.0006
##    360        0.4188             nan     0.1000   -0.0003
##    380        0.4028             nan     0.1000   -0.0001
##    400        0.3874             nan     0.1000   -0.0001
##    420        0.3734             nan     0.1000   -0.0003
##    440        0.3585             nan     0.1000   -0.0001
##    460        0.3458             nan     0.1000   -0.0001
##    480        0.3342             nan     0.1000   -0.0003
##    500        0.3217             nan     0.1000   -0.0003
## 
## - Fold03: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3282             nan     0.1000    0.0170
##      2        1.2972             nan     0.1000    0.0144
##      3        1.2707             nan     0.1000    0.0114
##      4        1.2453             nan     0.1000    0.0112
##      5        1.2245             nan     0.1000    0.0087
##      6        1.2062             nan     0.1000    0.0079
##      7        1.1907             nan     0.1000    0.0057
##      8        1.1754             nan     0.1000    0.0061
##      9        1.1627             nan     0.1000    0.0034
##     10        1.1495             nan     0.1000    0.0052
##     20        1.0401             nan     0.1000    0.0028
##     40        0.9022             nan     0.1000    0.0012
##     60        0.8176             nan     0.1000    0.0003
##     80        0.7548             nan     0.1000    0.0008
##    100        0.6994             nan     0.1000    0.0000
##    120        0.6551             nan     0.1000    0.0001
##    140        0.6154             nan     0.1000    0.0001
##    160        0.5814             nan     0.1000   -0.0004
##    180        0.5512             nan     0.1000   -0.0004
##    200        0.5231             nan     0.1000   -0.0000
##    220        0.4982             nan     0.1000   -0.0001
##    240        0.4740             nan     0.1000   -0.0001
##    260        0.4506             nan     0.1000   -0.0005
##    280        0.4299             nan     0.1000   -0.0004
##    300        0.4091             nan     0.1000   -0.0005
##    320        0.3912             nan     0.1000   -0.0001
##    340        0.3750             nan     0.1000   -0.0004
##    360        0.3601             nan     0.1000   -0.0001
##    380        0.3446             nan     0.1000   -0.0002
##    400        0.3314             nan     0.1000   -0.0005
##    420        0.3157             nan     0.1000   -0.0001
##    440        0.3027             nan     0.1000   -0.0003
##    460        0.2895             nan     0.1000   -0.0002
##    480        0.2770             nan     0.1000   -0.0003
##    500        0.2664             nan     0.1000   -0.0002
## 
## - Fold03: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3267             nan     0.1000    0.0178
##      2        1.2960             nan     0.1000    0.0123
##      3        1.2639             nan     0.1000    0.0146
##      4        1.2391             nan     0.1000    0.0113
##      5        1.2158             nan     0.1000    0.0097
##      6        1.1943             nan     0.1000    0.0081
##      7        1.1778             nan     0.1000    0.0060
##      8        1.1617             nan     0.1000    0.0063
##      9        1.1458             nan     0.1000    0.0068
##     10        1.1322             nan     0.1000    0.0048
##     20        1.0297             nan     0.1000    0.0015
##     40        0.8821             nan     0.1000   -0.0001
##     60        0.8016             nan     0.1000    0.0000
##     80        0.7297             nan     0.1000   -0.0006
##    100        0.6728             nan     0.1000    0.0001
##    120        0.6268             nan     0.1000   -0.0007
##    140        0.5870             nan     0.1000   -0.0007
##    160        0.5466             nan     0.1000   -0.0002
##    180        0.5107             nan     0.1000    0.0000
##    200        0.4808             nan     0.1000   -0.0007
##    220        0.4551             nan     0.1000   -0.0008
##    240        0.4301             nan     0.1000   -0.0002
##    260        0.4082             nan     0.1000   -0.0006
##    280        0.3860             nan     0.1000   -0.0002
##    300        0.3659             nan     0.1000    0.0000
##    320        0.3486             nan     0.1000   -0.0005
##    340        0.3304             nan     0.1000   -0.0002
##    360        0.3135             nan     0.1000   -0.0002
##    380        0.2984             nan     0.1000   -0.0001
##    400        0.2849             nan     0.1000   -0.0003
##    420        0.2711             nan     0.1000   -0.0004
##    440        0.2580             nan     0.1000   -0.0001
##    460        0.2466             nan     0.1000   -0.0004
##    480        0.2353             nan     0.1000   -0.0003
##    500        0.2249             nan     0.1000   -0.0004
## 
## - Fold03: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3274             nan     0.1000    0.0182
##      2        1.2955             nan     0.1000    0.0145
##      3        1.2659             nan     0.1000    0.0125
##      4        1.2405             nan     0.1000    0.0097
##      5        1.2188             nan     0.1000    0.0106
##      6        1.1973             nan     0.1000    0.0091
##      7        1.1796             nan     0.1000    0.0072
##      8        1.1621             nan     0.1000    0.0070
##      9        1.1465             nan     0.1000    0.0056
##     10        1.1315             nan     0.1000    0.0064
##     20        1.0092             nan     0.1000    0.0047
##     40        0.8701             nan     0.1000    0.0019
##     60        0.7757             nan     0.1000   -0.0002
##     80        0.7109             nan     0.1000   -0.0004
##    100        0.6530             nan     0.1000    0.0002
##    120        0.6014             nan     0.1000   -0.0002
##    140        0.5541             nan     0.1000   -0.0002
##    160        0.5157             nan     0.1000    0.0001
##    180        0.4852             nan     0.1000   -0.0005
##    200        0.4522             nan     0.1000   -0.0006
##    220        0.4244             nan     0.1000   -0.0003
##    240        0.3986             nan     0.1000   -0.0003
##    260        0.3765             nan     0.1000   -0.0003
##    280        0.3545             nan     0.1000   -0.0003
##    300        0.3327             nan     0.1000   -0.0004
##    320        0.3137             nan     0.1000   -0.0003
##    340        0.2967             nan     0.1000   -0.0003
##    360        0.2817             nan     0.1000   -0.0005
##    380        0.2673             nan     0.1000   -0.0005
##    400        0.2524             nan     0.1000   -0.0002
##    420        0.2397             nan     0.1000   -0.0002
##    440        0.2279             nan     0.1000   -0.0002
##    460        0.2166             nan     0.1000   -0.0004
##    480        0.2045             nan     0.1000   -0.0004
##    500        0.1938             nan     0.1000   -0.0002
## 
## - Fold03: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold03: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3259             nan     0.1000    0.0191
##      2        1.2922             nan     0.1000    0.0149
##      3        1.2658             nan     0.1000    0.0111
##      4        1.2369             nan     0.1000    0.0130
##      5        1.2161             nan     0.1000    0.0084
##      6        1.1935             nan     0.1000    0.0095
##      7        1.1731             nan     0.1000    0.0077
##      8        1.1554             nan     0.1000    0.0062
##      9        1.1374             nan     0.1000    0.0080
##     10        1.1210             nan     0.1000    0.0057
##     20        1.0061             nan     0.1000    0.0029
##     40        0.8519             nan     0.1000    0.0023
##     60        0.7531             nan     0.1000    0.0009
##     80        0.6777             nan     0.1000    0.0007
##    100        0.6194             nan     0.1000   -0.0003
##    120        0.5684             nan     0.1000   -0.0002
##    140        0.5213             nan     0.1000   -0.0004
##    160        0.4804             nan     0.1000   -0.0005
##    180        0.4459             nan     0.1000   -0.0001
##    200        0.4153             nan     0.1000   -0.0002
##    220        0.3869             nan     0.1000   -0.0001
##    240        0.3597             nan     0.1000   -0.0000
##    260        0.3346             nan     0.1000   -0.0002
##    280        0.3145             nan     0.1000   -0.0006
##    300        0.2955             nan     0.1000   -0.0004
##    320        0.2775             nan     0.1000   -0.0004
##    340        0.2588             nan     0.1000   -0.0004
##    360        0.2444             nan     0.1000   -0.0002
##    380        0.2290             nan     0.1000   -0.0003
##    400        0.2144             nan     0.1000   -0.0002
##    420        0.2026             nan     0.1000   -0.0002
##    440        0.1909             nan     0.1000   -0.0001
##    460        0.1798             nan     0.1000   -0.0003
##    480        0.1700             nan     0.1000   -0.0001
##    500        0.1606             nan     0.1000   -0.0002
## 
## - Fold03: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3485             nan     0.1000    0.0083
##      2        1.3336             nan     0.1000    0.0070
##      3        1.3204             nan     0.1000    0.0062
##      4        1.3088             nan     0.1000    0.0051
##      5        1.2985             nan     0.1000    0.0050
##      6        1.2893             nan     0.1000    0.0039
##      7        1.2816             nan     0.1000    0.0040
##      8        1.2743             nan     0.1000    0.0030
##      9        1.2679             nan     0.1000    0.0023
##     10        1.2609             nan     0.1000    0.0028
##     20        1.2187             nan     0.1000    0.0012
##     40        1.1743             nan     0.1000    0.0001
##     60        1.1487             nan     0.1000    0.0003
##     80        1.1281             nan     0.1000   -0.0000
##    100        1.1129             nan     0.1000    0.0001
##    120        1.1001             nan     0.1000   -0.0002
##    140        1.0901             nan     0.1000   -0.0001
##    160        1.0796             nan     0.1000    0.0000
##    180        1.0709             nan     0.1000   -0.0000
##    200        1.0622             nan     0.1000    0.0000
##    220        1.0535             nan     0.1000   -0.0001
##    240        1.0463             nan     0.1000   -0.0003
##    260        1.0393             nan     0.1000   -0.0002
##    280        1.0323             nan     0.1000   -0.0002
##    300        1.0245             nan     0.1000   -0.0002
##    320        1.0178             nan     0.1000   -0.0002
##    340        1.0114             nan     0.1000    0.0000
##    360        1.0052             nan     0.1000   -0.0002
##    380        0.9993             nan     0.1000   -0.0001
##    400        0.9933             nan     0.1000   -0.0003
##    420        0.9878             nan     0.1000   -0.0002
##    440        0.9823             nan     0.1000   -0.0002
##    460        0.9775             nan     0.1000   -0.0002
##    480        0.9729             nan     0.1000   -0.0001
##    500        0.9685             nan     0.1000   -0.0001
## 
## - Fold04: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3432             nan     0.1000    0.0093
##      2        1.3288             nan     0.1000    0.0065
##      3        1.3121             nan     0.1000    0.0070
##      4        1.2996             nan     0.1000    0.0060
##      5        1.2863             nan     0.1000    0.0057
##      6        1.2775             nan     0.1000    0.0041
##      7        1.2678             nan     0.1000    0.0039
##      8        1.2595             nan     0.1000    0.0034
##      9        1.2497             nan     0.1000    0.0042
##     10        1.2419             nan     0.1000    0.0035
##     20        1.1781             nan     0.1000    0.0033
##     40        1.1133             nan     0.1000    0.0025
##     60        1.0615             nan     0.1000    0.0007
##     80        1.0280             nan     0.1000   -0.0003
##    100        0.9970             nan     0.1000    0.0010
##    120        0.9671             nan     0.1000    0.0009
##    140        0.9452             nan     0.1000   -0.0002
##    160        0.9226             nan     0.1000   -0.0003
##    180        0.9021             nan     0.1000   -0.0003
##    200        0.8844             nan     0.1000   -0.0007
##    220        0.8680             nan     0.1000    0.0001
##    240        0.8540             nan     0.1000   -0.0006
##    260        0.8385             nan     0.1000   -0.0001
##    280        0.8251             nan     0.1000   -0.0001
##    300        0.8144             nan     0.1000   -0.0002
##    320        0.8025             nan     0.1000   -0.0005
##    340        0.7893             nan     0.1000    0.0000
##    360        0.7800             nan     0.1000   -0.0003
##    380        0.7689             nan     0.1000   -0.0002
##    400        0.7585             nan     0.1000   -0.0002
##    420        0.7489             nan     0.1000    0.0005
##    440        0.7381             nan     0.1000   -0.0004
##    460        0.7284             nan     0.1000   -0.0001
##    480        0.7180             nan     0.1000   -0.0001
##    500        0.7105             nan     0.1000   -0.0007
## 
## - Fold04: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3365             nan     0.1000    0.0132
##      2        1.3158             nan     0.1000    0.0099
##      3        1.2940             nan     0.1000    0.0105
##      4        1.2775             nan     0.1000    0.0077
##      5        1.2657             nan     0.1000    0.0044
##      6        1.2528             nan     0.1000    0.0056
##      7        1.2390             nan     0.1000    0.0062
##      8        1.2265             nan     0.1000    0.0060
##      9        1.2151             nan     0.1000    0.0046
##     10        1.2049             nan     0.1000    0.0038
##     20        1.1321             nan     0.1000    0.0014
##     40        1.0420             nan     0.1000    0.0008
##     60        0.9930             nan     0.1000    0.0002
##     80        0.9467             nan     0.1000    0.0007
##    100        0.9073             nan     0.1000    0.0002
##    120        0.8705             nan     0.1000    0.0001
##    140        0.8363             nan     0.1000    0.0010
##    160        0.8116             nan     0.1000   -0.0002
##    180        0.7886             nan     0.1000    0.0005
##    200        0.7636             nan     0.1000    0.0003
##    220        0.7464             nan     0.1000    0.0000
##    240        0.7284             nan     0.1000   -0.0001
##    260        0.7138             nan     0.1000   -0.0001
##    280        0.6966             nan     0.1000   -0.0001
##    300        0.6797             nan     0.1000    0.0005
##    320        0.6653             nan     0.1000   -0.0000
##    340        0.6520             nan     0.1000   -0.0000
##    360        0.6379             nan     0.1000    0.0004
##    380        0.6236             nan     0.1000   -0.0005
##    400        0.6124             nan     0.1000   -0.0002
##    420        0.5993             nan     0.1000   -0.0004
##    440        0.5878             nan     0.1000   -0.0003
##    460        0.5786             nan     0.1000   -0.0007
##    480        0.5682             nan     0.1000   -0.0001
##    500        0.5599             nan     0.1000   -0.0001
## 
## - Fold04: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3338             nan     0.1000    0.0148
##      2        1.3084             nan     0.1000    0.0113
##      3        1.2868             nan     0.1000    0.0093
##      4        1.2665             nan     0.1000    0.0093
##      5        1.2483             nan     0.1000    0.0075
##      6        1.2338             nan     0.1000    0.0052
##      7        1.2207             nan     0.1000    0.0055
##      8        1.2071             nan     0.1000    0.0051
##      9        1.1942             nan     0.1000    0.0053
##     10        1.1820             nan     0.1000    0.0053
##     20        1.1011             nan     0.1000    0.0032
##     40        0.9980             nan     0.1000    0.0019
##     60        0.9385             nan     0.1000    0.0004
##     80        0.8841             nan     0.1000    0.0005
##    100        0.8384             nan     0.1000   -0.0003
##    120        0.8022             nan     0.1000   -0.0004
##    140        0.7728             nan     0.1000   -0.0005
##    160        0.7458             nan     0.1000   -0.0002
##    180        0.7187             nan     0.1000   -0.0004
##    200        0.6954             nan     0.1000   -0.0004
##    220        0.6751             nan     0.1000   -0.0004
##    240        0.6554             nan     0.1000    0.0000
##    260        0.6378             nan     0.1000   -0.0004
##    280        0.6176             nan     0.1000    0.0004
##    300        0.5982             nan     0.1000   -0.0001
##    320        0.5807             nan     0.1000   -0.0002
##    340        0.5658             nan     0.1000   -0.0004
##    360        0.5530             nan     0.1000   -0.0007
##    380        0.5399             nan     0.1000   -0.0004
##    400        0.5255             nan     0.1000   -0.0005
##    420        0.5130             nan     0.1000   -0.0004
##    440        0.4999             nan     0.1000    0.0001
##    460        0.4879             nan     0.1000   -0.0002
##    480        0.4780             nan     0.1000   -0.0004
##    500        0.4656             nan     0.1000   -0.0003
## 
## - Fold04: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3338             nan     0.1000    0.0145
##      2        1.3070             nan     0.1000    0.0109
##      3        1.2880             nan     0.1000    0.0083
##      4        1.2686             nan     0.1000    0.0092
##      5        1.2478             nan     0.1000    0.0089
##      6        1.2305             nan     0.1000    0.0074
##      7        1.2137             nan     0.1000    0.0075
##      8        1.1969             nan     0.1000    0.0074
##      9        1.1825             nan     0.1000    0.0061
##     10        1.1703             nan     0.1000    0.0053
##     20        1.0871             nan     0.1000    0.0007
##     40        0.9784             nan     0.1000    0.0008
##     60        0.9051             nan     0.1000    0.0004
##     80        0.8399             nan     0.1000    0.0019
##    100        0.7896             nan     0.1000    0.0000
##    120        0.7488             nan     0.1000   -0.0002
##    140        0.7153             nan     0.1000    0.0006
##    160        0.6827             nan     0.1000   -0.0001
##    180        0.6560             nan     0.1000   -0.0003
##    200        0.6276             nan     0.1000    0.0000
##    220        0.6033             nan     0.1000   -0.0003
##    240        0.5817             nan     0.1000   -0.0001
##    260        0.5609             nan     0.1000   -0.0001
##    280        0.5402             nan     0.1000   -0.0005
##    300        0.5236             nan     0.1000   -0.0001
##    320        0.5062             nan     0.1000   -0.0002
##    340        0.4888             nan     0.1000   -0.0003
##    360        0.4710             nan     0.1000   -0.0001
##    380        0.4574             nan     0.1000    0.0001
##    400        0.4439             nan     0.1000   -0.0003
##    420        0.4297             nan     0.1000   -0.0005
##    440        0.4167             nan     0.1000   -0.0004
##    460        0.4042             nan     0.1000   -0.0001
##    480        0.3930             nan     0.1000   -0.0003
##    500        0.3816             nan     0.1000   -0.0001
## 
## - Fold04: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3331             nan     0.1000    0.0149
##      2        1.3059             nan     0.1000    0.0127
##      3        1.2784             nan     0.1000    0.0133
##      4        1.2576             nan     0.1000    0.0079
##      5        1.2404             nan     0.1000    0.0070
##      6        1.2254             nan     0.1000    0.0065
##      7        1.2090             nan     0.1000    0.0072
##      8        1.1936             nan     0.1000    0.0063
##      9        1.1780             nan     0.1000    0.0066
##     10        1.1659             nan     0.1000    0.0040
##     20        1.0624             nan     0.1000    0.0019
##     40        0.9394             nan     0.1000    0.0017
##     60        0.8549             nan     0.1000    0.0010
##     80        0.7948             nan     0.1000    0.0003
##    100        0.7499             nan     0.1000    0.0002
##    120        0.7060             nan     0.1000   -0.0003
##    140        0.6674             nan     0.1000    0.0004
##    160        0.6357             nan     0.1000    0.0002
##    180        0.6055             nan     0.1000   -0.0001
##    200        0.5757             nan     0.1000   -0.0002
##    220        0.5495             nan     0.1000   -0.0001
##    240        0.5251             nan     0.1000   -0.0000
##    260        0.5030             nan     0.1000   -0.0003
##    280        0.4814             nan     0.1000   -0.0001
##    300        0.4616             nan     0.1000   -0.0001
##    320        0.4432             nan     0.1000   -0.0003
##    340        0.4251             nan     0.1000   -0.0001
##    360        0.4096             nan     0.1000   -0.0000
##    380        0.3937             nan     0.1000   -0.0001
##    400        0.3809             nan     0.1000   -0.0005
##    420        0.3684             nan     0.1000   -0.0006
##    440        0.3556             nan     0.1000   -0.0003
##    460        0.3424             nan     0.1000   -0.0003
##    480        0.3310             nan     0.1000   -0.0002
##    500        0.3195             nan     0.1000   -0.0002
## 
## - Fold04: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3326             nan     0.1000    0.0153
##      2        1.3017             nan     0.1000    0.0132
##      3        1.2757             nan     0.1000    0.0120
##      4        1.2506             nan     0.1000    0.0113
##      5        1.2304             nan     0.1000    0.0087
##      6        1.2097             nan     0.1000    0.0084
##      7        1.1915             nan     0.1000    0.0071
##      8        1.1756             nan     0.1000    0.0066
##      9        1.1631             nan     0.1000    0.0047
##     10        1.1455             nan     0.1000    0.0073
##     20        1.0446             nan     0.1000    0.0023
##     40        0.8964             nan     0.1000    0.0009
##     60        0.8186             nan     0.1000    0.0004
##     80        0.7558             nan     0.1000   -0.0002
##    100        0.6981             nan     0.1000    0.0009
##    120        0.6547             nan     0.1000   -0.0002
##    140        0.6163             nan     0.1000   -0.0002
##    160        0.5833             nan     0.1000   -0.0007
##    180        0.5518             nan     0.1000   -0.0003
##    200        0.5243             nan     0.1000   -0.0001
##    220        0.4973             nan     0.1000   -0.0001
##    240        0.4730             nan     0.1000   -0.0002
##    260        0.4489             nan     0.1000   -0.0005
##    280        0.4280             nan     0.1000   -0.0005
##    300        0.4088             nan     0.1000   -0.0005
##    320        0.3895             nan     0.1000   -0.0004
##    340        0.3731             nan     0.1000   -0.0003
##    360        0.3579             nan     0.1000   -0.0005
##    380        0.3422             nan     0.1000   -0.0004
##    400        0.3278             nan     0.1000   -0.0005
##    420        0.3139             nan     0.1000   -0.0001
##    440        0.3021             nan     0.1000   -0.0001
##    460        0.2887             nan     0.1000   -0.0002
##    480        0.2770             nan     0.1000   -0.0004
##    500        0.2651             nan     0.1000   -0.0001
## 
## - Fold04: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3254             nan     0.1000    0.0177
##      2        1.2954             nan     0.1000    0.0127
##      3        1.2699             nan     0.1000    0.0097
##      4        1.2453             nan     0.1000    0.0105
##      5        1.2212             nan     0.1000    0.0109
##      6        1.1991             nan     0.1000    0.0089
##      7        1.1799             nan     0.1000    0.0077
##      8        1.1632             nan     0.1000    0.0058
##      9        1.1465             nan     0.1000    0.0058
##     10        1.1300             nan     0.1000    0.0070
##     20        1.0264             nan     0.1000    0.0024
##     40        0.8851             nan     0.1000    0.0015
##     60        0.7988             nan     0.1000    0.0001
##     80        0.7314             nan     0.1000   -0.0002
##    100        0.6807             nan     0.1000   -0.0004
##    120        0.6275             nan     0.1000    0.0003
##    140        0.5874             nan     0.1000   -0.0004
##    160        0.5523             nan     0.1000   -0.0007
##    180        0.5185             nan     0.1000   -0.0001
##    200        0.4886             nan     0.1000   -0.0007
##    220        0.4596             nan     0.1000   -0.0006
##    240        0.4356             nan     0.1000   -0.0007
##    260        0.4120             nan     0.1000   -0.0002
##    280        0.3906             nan     0.1000   -0.0002
##    300        0.3712             nan     0.1000   -0.0004
##    320        0.3516             nan     0.1000   -0.0005
##    340        0.3345             nan     0.1000   -0.0005
##    360        0.3176             nan     0.1000   -0.0003
##    380        0.3012             nan     0.1000   -0.0002
##    400        0.2867             nan     0.1000   -0.0004
##    420        0.2723             nan     0.1000   -0.0002
##    440        0.2605             nan     0.1000   -0.0003
##    460        0.2470             nan     0.1000   -0.0003
##    480        0.2358             nan     0.1000   -0.0005
##    500        0.2251             nan     0.1000    0.0000
## 
## - Fold04: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3264             nan     0.1000    0.0178
##      2        1.2878             nan     0.1000    0.0173
##      3        1.2607             nan     0.1000    0.0116
##      4        1.2309             nan     0.1000    0.0132
##      5        1.2058             nan     0.1000    0.0103
##      6        1.1797             nan     0.1000    0.0115
##      7        1.1593             nan     0.1000    0.0092
##      8        1.1411             nan     0.1000    0.0084
##      9        1.1254             nan     0.1000    0.0053
##     10        1.1123             nan     0.1000    0.0051
##     20        0.9944             nan     0.1000    0.0031
##     40        0.8422             nan     0.1000    0.0014
##     60        0.7512             nan     0.1000   -0.0003
##     80        0.6794             nan     0.1000   -0.0008
##    100        0.6207             nan     0.1000   -0.0002
##    120        0.5719             nan     0.1000    0.0002
##    140        0.5308             nan     0.1000   -0.0005
##    160        0.4939             nan     0.1000   -0.0003
##    180        0.4597             nan     0.1000   -0.0002
##    200        0.4292             nan     0.1000   -0.0005
##    220        0.4022             nan     0.1000   -0.0005
##    240        0.3779             nan     0.1000   -0.0003
##    260        0.3554             nan     0.1000   -0.0004
##    280        0.3335             nan     0.1000   -0.0003
##    300        0.3152             nan     0.1000   -0.0004
##    320        0.2963             nan     0.1000   -0.0006
##    340        0.2804             nan     0.1000   -0.0003
##    360        0.2661             nan     0.1000   -0.0002
##    380        0.2510             nan     0.1000   -0.0003
##    400        0.2377             nan     0.1000   -0.0004
##    420        0.2250             nan     0.1000   -0.0003
##    440        0.2126             nan     0.1000   -0.0003
##    460        0.2007             nan     0.1000   -0.0002
##    480        0.1898             nan     0.1000   -0.0001
##    500        0.1809             nan     0.1000   -0.0003
## 
## - Fold04: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold04: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3225             nan     0.1000    0.0191
##      2        1.2905             nan     0.1000    0.0148
##      3        1.2581             nan     0.1000    0.0139
##      4        1.2247             nan     0.1000    0.0154
##      5        1.2030             nan     0.1000    0.0088
##      6        1.1855             nan     0.1000    0.0069
##      7        1.1646             nan     0.1000    0.0081
##      8        1.1461             nan     0.1000    0.0079
##      9        1.1299             nan     0.1000    0.0060
##     10        1.1098             nan     0.1000    0.0081
##     20        0.9882             nan     0.1000    0.0026
##     40        0.8360             nan     0.1000    0.0010
##     60        0.7388             nan     0.1000    0.0013
##     80        0.6700             nan     0.1000   -0.0007
##    100        0.6133             nan     0.1000   -0.0006
##    120        0.5623             nan     0.1000   -0.0003
##    140        0.5182             nan     0.1000   -0.0003
##    160        0.4748             nan     0.1000   -0.0002
##    180        0.4418             nan     0.1000   -0.0007
##    200        0.4108             nan     0.1000   -0.0006
##    220        0.3851             nan     0.1000   -0.0004
##    240        0.3574             nan     0.1000   -0.0002
##    260        0.3349             nan     0.1000   -0.0004
##    280        0.3138             nan     0.1000   -0.0002
##    300        0.2944             nan     0.1000   -0.0004
##    320        0.2769             nan     0.1000   -0.0005
##    340        0.2604             nan     0.1000   -0.0002
##    360        0.2450             nan     0.1000   -0.0003
##    380        0.2302             nan     0.1000   -0.0002
##    400        0.2175             nan     0.1000   -0.0001
##    420        0.2048             nan     0.1000   -0.0003
##    440        0.1930             nan     0.1000   -0.0001
##    460        0.1824             nan     0.1000   -0.0003
##    480        0.1714             nan     0.1000   -0.0003
##    500        0.1614             nan     0.1000   -0.0003
## 
## - Fold04: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3503             nan     0.1000    0.0083
##      2        1.3354             nan     0.1000    0.0068
##      3        1.3230             nan     0.1000    0.0060
##      4        1.3116             nan     0.1000    0.0051
##      5        1.3011             nan     0.1000    0.0050
##      6        1.2928             nan     0.1000    0.0044
##      7        1.2855             nan     0.1000    0.0032
##      8        1.2791             nan     0.1000    0.0029
##      9        1.2723             nan     0.1000    0.0029
##     10        1.2661             nan     0.1000    0.0025
##     20        1.2261             nan     0.1000    0.0011
##     40        1.1798             nan     0.1000    0.0006
##     60        1.1502             nan     0.1000    0.0004
##     80        1.1306             nan     0.1000    0.0003
##    100        1.1146             nan     0.1000    0.0001
##    120        1.1010             nan     0.1000   -0.0000
##    140        1.0900             nan     0.1000    0.0000
##    160        1.0792             nan     0.1000   -0.0003
##    180        1.0708             nan     0.1000   -0.0007
##    200        1.0622             nan     0.1000    0.0001
##    220        1.0548             nan     0.1000   -0.0000
##    240        1.0473             nan     0.1000   -0.0001
##    260        1.0396             nan     0.1000    0.0001
##    280        1.0328             nan     0.1000   -0.0001
##    300        1.0276             nan     0.1000   -0.0003
##    320        1.0210             nan     0.1000    0.0000
##    340        1.0153             nan     0.1000    0.0000
##    360        1.0096             nan     0.1000    0.0000
##    380        1.0045             nan     0.1000   -0.0002
##    400        0.9996             nan     0.1000   -0.0002
##    420        0.9941             nan     0.1000   -0.0002
##    440        0.9889             nan     0.1000    0.0000
##    460        0.9844             nan     0.1000   -0.0001
##    480        0.9786             nan     0.1000   -0.0001
##    500        0.9736             nan     0.1000   -0.0003
## 
## - Fold05: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3446             nan     0.1000    0.0092
##      2        1.3262             nan     0.1000    0.0089
##      3        1.3109             nan     0.1000    0.0071
##      4        1.2953             nan     0.1000    0.0071
##      5        1.2832             nan     0.1000    0.0052
##      6        1.2734             nan     0.1000    0.0043
##      7        1.2620             nan     0.1000    0.0052
##      8        1.2522             nan     0.1000    0.0045
##      9        1.2442             nan     0.1000    0.0038
##     10        1.2365             nan     0.1000    0.0037
##     20        1.1709             nan     0.1000    0.0013
##     40        1.0981             nan     0.1000    0.0010
##     60        1.0514             nan     0.1000    0.0010
##     80        1.0168             nan     0.1000   -0.0000
##    100        0.9819             nan     0.1000   -0.0003
##    120        0.9522             nan     0.1000    0.0005
##    140        0.9309             nan     0.1000    0.0003
##    160        0.9052             nan     0.1000    0.0006
##    180        0.8858             nan     0.1000    0.0003
##    200        0.8682             nan     0.1000    0.0003
##    220        0.8547             nan     0.1000   -0.0001
##    240        0.8344             nan     0.1000    0.0001
##    260        0.8233             nan     0.1000   -0.0003
##    280        0.8057             nan     0.1000   -0.0004
##    300        0.7942             nan     0.1000   -0.0002
##    320        0.7842             nan     0.1000   -0.0002
##    340        0.7760             nan     0.1000   -0.0000
##    360        0.7659             nan     0.1000   -0.0007
##    380        0.7545             nan     0.1000   -0.0003
##    400        0.7442             nan     0.1000   -0.0004
##    420        0.7366             nan     0.1000   -0.0003
##    440        0.7285             nan     0.1000   -0.0003
##    460        0.7193             nan     0.1000   -0.0002
##    480        0.7114             nan     0.1000    0.0001
##    500        0.7011             nan     0.1000    0.0002
## 
## - Fold05: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3428             nan     0.1000    0.0102
##      2        1.3228             nan     0.1000    0.0096
##      3        1.3075             nan     0.1000    0.0074
##      4        1.2909             nan     0.1000    0.0074
##      5        1.2766             nan     0.1000    0.0067
##      6        1.2629             nan     0.1000    0.0063
##      7        1.2517             nan     0.1000    0.0050
##      8        1.2417             nan     0.1000    0.0042
##      9        1.2307             nan     0.1000    0.0045
##     10        1.2177             nan     0.1000    0.0055
##     20        1.1475             nan     0.1000    0.0019
##     40        1.0568             nan     0.1000    0.0003
##     60        0.9978             nan     0.1000    0.0001
##     80        0.9494             nan     0.1000    0.0004
##    100        0.9083             nan     0.1000   -0.0005
##    120        0.8775             nan     0.1000   -0.0001
##    140        0.8487             nan     0.1000   -0.0001
##    160        0.8227             nan     0.1000   -0.0001
##    180        0.8001             nan     0.1000   -0.0001
##    200        0.7787             nan     0.1000    0.0001
##    220        0.7566             nan     0.1000   -0.0002
##    240        0.7384             nan     0.1000   -0.0002
##    260        0.7202             nan     0.1000   -0.0000
##    280        0.7063             nan     0.1000   -0.0002
##    300        0.6913             nan     0.1000   -0.0003
##    320        0.6765             nan     0.1000    0.0001
##    340        0.6620             nan     0.1000    0.0002
##    360        0.6482             nan     0.1000   -0.0002
##    380        0.6369             nan     0.1000   -0.0002
##    400        0.6245             nan     0.1000    0.0001
##    420        0.6125             nan     0.1000    0.0000
##    440        0.6010             nan     0.1000   -0.0003
##    460        0.5892             nan     0.1000   -0.0002
##    480        0.5786             nan     0.1000   -0.0000
##    500        0.5685             nan     0.1000   -0.0002
## 
## - Fold05: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3407             nan     0.1000    0.0131
##      2        1.3161             nan     0.1000    0.0116
##      3        1.2919             nan     0.1000    0.0109
##      4        1.2730             nan     0.1000    0.0086
##      5        1.2541             nan     0.1000    0.0092
##      6        1.2374             nan     0.1000    0.0074
##      7        1.2236             nan     0.1000    0.0048
##      8        1.2093             nan     0.1000    0.0058
##      9        1.1978             nan     0.1000    0.0054
##     10        1.1883             nan     0.1000    0.0038
##     20        1.1057             nan     0.1000    0.0016
##     40        1.0071             nan     0.1000    0.0018
##     60        0.9423             nan     0.1000   -0.0003
##     80        0.8948             nan     0.1000    0.0007
##    100        0.8462             nan     0.1000    0.0004
##    120        0.8004             nan     0.1000    0.0001
##    140        0.7662             nan     0.1000   -0.0001
##    160        0.7383             nan     0.1000    0.0004
##    180        0.7133             nan     0.1000   -0.0001
##    200        0.6915             nan     0.1000   -0.0003
##    220        0.6687             nan     0.1000   -0.0003
##    240        0.6467             nan     0.1000   -0.0003
##    260        0.6273             nan     0.1000   -0.0001
##    280        0.6119             nan     0.1000   -0.0003
##    300        0.5967             nan     0.1000    0.0001
##    320        0.5805             nan     0.1000   -0.0004
##    340        0.5647             nan     0.1000    0.0002
##    360        0.5508             nan     0.1000   -0.0003
##    380        0.5365             nan     0.1000   -0.0006
##    400        0.5222             nan     0.1000   -0.0003
##    420        0.5093             nan     0.1000   -0.0001
##    440        0.4975             nan     0.1000   -0.0002
##    460        0.4851             nan     0.1000   -0.0003
##    480        0.4745             nan     0.1000   -0.0003
##    500        0.4648             nan     0.1000   -0.0002
## 
## - Fold05: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3319             nan     0.1000    0.0161
##      2        1.3043             nan     0.1000    0.0128
##      3        1.2800             nan     0.1000    0.0110
##      4        1.2591             nan     0.1000    0.0079
##      5        1.2381             nan     0.1000    0.0100
##      6        1.2170             nan     0.1000    0.0092
##      7        1.2011             nan     0.1000    0.0065
##      8        1.1847             nan     0.1000    0.0064
##      9        1.1723             nan     0.1000    0.0045
##     10        1.1598             nan     0.1000    0.0038
##     20        1.0603             nan     0.1000    0.0046
##     40        0.9540             nan     0.1000    0.0002
##     60        0.8826             nan     0.1000    0.0006
##     80        0.8264             nan     0.1000    0.0005
##    100        0.7751             nan     0.1000    0.0003
##    120        0.7369             nan     0.1000   -0.0001
##    140        0.7024             nan     0.1000    0.0000
##    160        0.6710             nan     0.1000    0.0002
##    180        0.6430             nan     0.1000    0.0003
##    200        0.6136             nan     0.1000   -0.0003
##    220        0.5903             nan     0.1000   -0.0004
##    240        0.5665             nan     0.1000   -0.0002
##    260        0.5480             nan     0.1000   -0.0004
##    280        0.5292             nan     0.1000   -0.0005
##    300        0.5121             nan     0.1000   -0.0004
##    320        0.4966             nan     0.1000   -0.0006
##    340        0.4816             nan     0.1000   -0.0005
##    360        0.4674             nan     0.1000   -0.0002
##    380        0.4533             nan     0.1000   -0.0002
##    400        0.4395             nan     0.1000   -0.0002
##    420        0.4253             nan     0.1000   -0.0002
##    440        0.4140             nan     0.1000   -0.0004
##    460        0.4012             nan     0.1000   -0.0005
##    480        0.3916             nan     0.1000   -0.0004
##    500        0.3806             nan     0.1000   -0.0003
## 
## - Fold05: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3325             nan     0.1000    0.0161
##      2        1.3059             nan     0.1000    0.0111
##      3        1.2797             nan     0.1000    0.0120
##      4        1.2569             nan     0.1000    0.0084
##      5        1.2360             nan     0.1000    0.0083
##      6        1.2166             nan     0.1000    0.0087
##      7        1.2022             nan     0.1000    0.0056
##      8        1.1858             nan     0.1000    0.0066
##      9        1.1703             nan     0.1000    0.0056
##     10        1.1535             nan     0.1000    0.0078
##     20        1.0553             nan     0.1000    0.0032
##     40        0.9450             nan     0.1000    0.0002
##     60        0.8645             nan     0.1000    0.0005
##     80        0.7994             nan     0.1000   -0.0003
##    100        0.7488             nan     0.1000    0.0005
##    120        0.7042             nan     0.1000    0.0002
##    140        0.6686             nan     0.1000   -0.0001
##    160        0.6325             nan     0.1000    0.0003
##    180        0.6002             nan     0.1000    0.0002
##    200        0.5726             nan     0.1000   -0.0002
##    220        0.5463             nan     0.1000    0.0002
##    240        0.5227             nan     0.1000   -0.0003
##    260        0.4989             nan     0.1000   -0.0003
##    280        0.4790             nan     0.1000   -0.0006
##    300        0.4583             nan     0.1000   -0.0006
##    320        0.4405             nan     0.1000   -0.0000
##    340        0.4237             nan     0.1000   -0.0001
##    360        0.4059             nan     0.1000   -0.0003
##    380        0.3916             nan     0.1000   -0.0002
##    400        0.3769             nan     0.1000   -0.0007
##    420        0.3644             nan     0.1000   -0.0001
##    440        0.3507             nan     0.1000   -0.0002
##    460        0.3368             nan     0.1000   -0.0001
##    480        0.3246             nan     0.1000   -0.0000
##    500        0.3132             nan     0.1000   -0.0002
## 
## - Fold05: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3332             nan     0.1000    0.0146
##      2        1.3069             nan     0.1000    0.0108
##      3        1.2777             nan     0.1000    0.0120
##      4        1.2542             nan     0.1000    0.0102
##      5        1.2341             nan     0.1000    0.0090
##      6        1.2131             nan     0.1000    0.0085
##      7        1.1951             nan     0.1000    0.0067
##      8        1.1773             nan     0.1000    0.0066
##      9        1.1620             nan     0.1000    0.0061
##     10        1.1491             nan     0.1000    0.0053
##     20        1.0475             nan     0.1000    0.0021
##     40        0.9151             nan     0.1000    0.0006
##     60        0.8376             nan     0.1000   -0.0001
##     80        0.7658             nan     0.1000    0.0015
##    100        0.7130             nan     0.1000   -0.0003
##    120        0.6672             nan     0.1000    0.0005
##    140        0.6291             nan     0.1000   -0.0003
##    160        0.5909             nan     0.1000    0.0004
##    180        0.5621             nan     0.1000   -0.0001
##    200        0.5323             nan     0.1000   -0.0004
##    220        0.5074             nan     0.1000   -0.0004
##    240        0.4829             nan     0.1000   -0.0004
##    260        0.4598             nan     0.1000   -0.0002
##    280        0.4390             nan     0.1000   -0.0007
##    300        0.4184             nan     0.1000   -0.0003
##    320        0.3985             nan     0.1000   -0.0006
##    340        0.3817             nan     0.1000   -0.0003
##    360        0.3649             nan     0.1000   -0.0003
##    380        0.3498             nan     0.1000   -0.0003
##    400        0.3356             nan     0.1000   -0.0004
##    420        0.3187             nan     0.1000   -0.0002
##    440        0.3054             nan     0.1000   -0.0002
##    460        0.2924             nan     0.1000   -0.0002
##    480        0.2809             nan     0.1000   -0.0001
##    500        0.2684             nan     0.1000   -0.0003
## 
## - Fold05: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3282             nan     0.1000    0.0175
##      2        1.2945             nan     0.1000    0.0146
##      3        1.2638             nan     0.1000    0.0130
##      4        1.2379             nan     0.1000    0.0102
##      5        1.2195             nan     0.1000    0.0072
##      6        1.1989             nan     0.1000    0.0083
##      7        1.1821             nan     0.1000    0.0064
##      8        1.1662             nan     0.1000    0.0063
##      9        1.1507             nan     0.1000    0.0063
##     10        1.1339             nan     0.1000    0.0071
##     20        1.0221             nan     0.1000    0.0033
##     40        0.8948             nan     0.1000    0.0004
##     60        0.8054             nan     0.1000    0.0008
##     80        0.7364             nan     0.1000    0.0001
##    100        0.6814             nan     0.1000   -0.0001
##    120        0.6323             nan     0.1000   -0.0002
##    140        0.5919             nan     0.1000   -0.0003
##    160        0.5563             nan     0.1000   -0.0002
##    180        0.5210             nan     0.1000   -0.0002
##    200        0.4924             nan     0.1000    0.0000
##    220        0.4656             nan     0.1000   -0.0002
##    240        0.4413             nan     0.1000    0.0003
##    260        0.4169             nan     0.1000    0.0001
##    280        0.3943             nan     0.1000   -0.0002
##    300        0.3741             nan     0.1000   -0.0001
##    320        0.3550             nan     0.1000   -0.0003
##    340        0.3368             nan     0.1000   -0.0002
##    360        0.3205             nan     0.1000   -0.0005
##    380        0.3061             nan     0.1000   -0.0004
##    400        0.2902             nan     0.1000   -0.0003
##    420        0.2757             nan     0.1000   -0.0001
##    440        0.2626             nan     0.1000   -0.0002
##    460        0.2511             nan     0.1000   -0.0001
##    480        0.2403             nan     0.1000   -0.0001
##    500        0.2298             nan     0.1000   -0.0003
## 
## - Fold05: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3262             nan     0.1000    0.0172
##      2        1.2953             nan     0.1000    0.0141
##      3        1.2659             nan     0.1000    0.0138
##      4        1.2423             nan     0.1000    0.0083
##      5        1.2174             nan     0.1000    0.0104
##      6        1.1949             nan     0.1000    0.0099
##      7        1.1756             nan     0.1000    0.0072
##      8        1.1530             nan     0.1000    0.0101
##      9        1.1361             nan     0.1000    0.0064
##     10        1.1183             nan     0.1000    0.0071
##     20        0.9980             nan     0.1000    0.0018
##     40        0.8437             nan     0.1000    0.0014
##     60        0.7526             nan     0.1000    0.0010
##     80        0.6904             nan     0.1000    0.0002
##    100        0.6278             nan     0.1000    0.0002
##    120        0.5816             nan     0.1000    0.0005
##    140        0.5357             nan     0.1000   -0.0001
##    160        0.4969             nan     0.1000   -0.0007
##    180        0.4636             nan     0.1000    0.0003
##    200        0.4349             nan     0.1000    0.0000
##    220        0.4066             nan     0.1000   -0.0001
##    240        0.3821             nan     0.1000   -0.0003
##    260        0.3591             nan     0.1000   -0.0002
##    280        0.3381             nan     0.1000   -0.0003
##    300        0.3177             nan     0.1000   -0.0003
##    320        0.3000             nan     0.1000   -0.0002
##    340        0.2823             nan     0.1000   -0.0001
##    360        0.2658             nan     0.1000   -0.0002
##    380        0.2505             nan     0.1000   -0.0004
##    400        0.2376             nan     0.1000   -0.0002
##    420        0.2264             nan     0.1000   -0.0003
##    440        0.2152             nan     0.1000   -0.0004
##    460        0.2021             nan     0.1000   -0.0003
##    480        0.1916             nan     0.1000   -0.0003
##    500        0.1814             nan     0.1000   -0.0002
## 
## - Fold05: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold05: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3251             nan     0.1000    0.0180
##      2        1.2953             nan     0.1000    0.0133
##      3        1.2692             nan     0.1000    0.0099
##      4        1.2423             nan     0.1000    0.0114
##      5        1.2192             nan     0.1000    0.0104
##      6        1.2006             nan     0.1000    0.0076
##      7        1.1783             nan     0.1000    0.0094
##      8        1.1561             nan     0.1000    0.0104
##      9        1.1384             nan     0.1000    0.0063
##     10        1.1229             nan     0.1000    0.0053
##     20        0.9929             nan     0.1000    0.0010
##     40        0.8426             nan     0.1000    0.0009
##     60        0.7487             nan     0.1000    0.0005
##     80        0.6762             nan     0.1000    0.0004
##    100        0.6136             nan     0.1000   -0.0003
##    120        0.5599             nan     0.1000    0.0002
##    140        0.5157             nan     0.1000    0.0000
##    160        0.4791             nan     0.1000   -0.0000
##    180        0.4443             nan     0.1000   -0.0001
##    200        0.4093             nan     0.1000   -0.0004
##    220        0.3807             nan     0.1000   -0.0002
##    240        0.3566             nan     0.1000   -0.0005
##    260        0.3322             nan     0.1000   -0.0001
##    280        0.3101             nan     0.1000   -0.0003
##    300        0.2902             nan     0.1000   -0.0002
##    320        0.2726             nan     0.1000   -0.0005
##    340        0.2565             nan     0.1000   -0.0002
##    360        0.2403             nan     0.1000   -0.0001
##    380        0.2249             nan     0.1000   -0.0001
##    400        0.2120             nan     0.1000   -0.0001
##    420        0.1994             nan     0.1000   -0.0001
##    440        0.1883             nan     0.1000   -0.0002
##    460        0.1770             nan     0.1000   -0.0004
##    480        0.1663             nan     0.1000   -0.0002
##    500        0.1573             nan     0.1000   -0.0002
## 
## - Fold05: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3500             nan     0.1000    0.0081
##      2        1.3354             nan     0.1000    0.0070
##      3        1.3224             nan     0.1000    0.0057
##      4        1.3104             nan     0.1000    0.0050
##      5        1.3005             nan     0.1000    0.0050
##      6        1.2919             nan     0.1000    0.0040
##      7        1.2847             nan     0.1000    0.0034
##      8        1.2763             nan     0.1000    0.0029
##      9        1.2713             nan     0.1000    0.0025
##     10        1.2652             nan     0.1000    0.0023
##     20        1.2229             nan     0.1000    0.0011
##     40        1.1783             nan     0.1000    0.0003
##     60        1.1489             nan     0.1000   -0.0001
##     80        1.1279             nan     0.1000    0.0001
##    100        1.1125             nan     0.1000   -0.0003
##    120        1.0987             nan     0.1000   -0.0000
##    140        1.0891             nan     0.1000   -0.0002
##    160        1.0786             nan     0.1000   -0.0002
##    180        1.0695             nan     0.1000   -0.0001
##    200        1.0615             nan     0.1000    0.0001
##    220        1.0533             nan     0.1000    0.0001
##    240        1.0449             nan     0.1000   -0.0001
##    260        1.0385             nan     0.1000    0.0000
##    280        1.0317             nan     0.1000   -0.0001
##    300        1.0252             nan     0.1000   -0.0004
##    320        1.0191             nan     0.1000   -0.0003
##    340        1.0130             nan     0.1000   -0.0004
##    360        1.0074             nan     0.1000   -0.0001
##    380        1.0020             nan     0.1000   -0.0002
##    400        0.9967             nan     0.1000   -0.0002
##    420        0.9909             nan     0.1000    0.0000
##    440        0.9859             nan     0.1000   -0.0000
##    460        0.9815             nan     0.1000   -0.0002
##    480        0.9763             nan     0.1000   -0.0002
##    500        0.9720             nan     0.1000   -0.0006
## 
## - Fold06: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3400             nan     0.1000    0.0115
##      2        1.3206             nan     0.1000    0.0092
##      3        1.3037             nan     0.1000    0.0075
##      4        1.2891             nan     0.1000    0.0065
##      5        1.2784             nan     0.1000    0.0050
##      6        1.2666             nan     0.1000    0.0053
##      7        1.2567             nan     0.1000    0.0047
##      8        1.2465             nan     0.1000    0.0045
##      9        1.2387             nan     0.1000    0.0037
##     10        1.2301             nan     0.1000    0.0037
##     20        1.1706             nan     0.1000    0.0009
##     40        1.0997             nan     0.1000    0.0004
##     60        1.0483             nan     0.1000   -0.0000
##     80        1.0131             nan     0.1000    0.0000
##    100        0.9787             nan     0.1000    0.0012
##    120        0.9535             nan     0.1000    0.0001
##    140        0.9264             nan     0.1000    0.0005
##    160        0.9061             nan     0.1000   -0.0000
##    180        0.8879             nan     0.1000   -0.0002
##    200        0.8690             nan     0.1000    0.0013
##    220        0.8517             nan     0.1000   -0.0001
##    240        0.8347             nan     0.1000   -0.0005
##    260        0.8226             nan     0.1000   -0.0000
##    280        0.8106             nan     0.1000   -0.0002
##    300        0.7994             nan     0.1000   -0.0001
##    320        0.7855             nan     0.1000   -0.0001
##    340        0.7740             nan     0.1000    0.0002
##    360        0.7619             nan     0.1000   -0.0002
##    380        0.7533             nan     0.1000    0.0001
##    400        0.7418             nan     0.1000   -0.0002
##    420        0.7304             nan     0.1000   -0.0003
##    440        0.7213             nan     0.1000   -0.0002
##    460        0.7099             nan     0.1000   -0.0004
##    480        0.7005             nan     0.1000   -0.0003
##    500        0.6945             nan     0.1000   -0.0002
## 
## - Fold06: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3422             nan     0.1000    0.0108
##      2        1.3232             nan     0.1000    0.0094
##      3        1.3077             nan     0.1000    0.0061
##      4        1.2932             nan     0.1000    0.0062
##      5        1.2778             nan     0.1000    0.0069
##      6        1.2643             nan     0.1000    0.0052
##      7        1.2524             nan     0.1000    0.0054
##      8        1.2424             nan     0.1000    0.0033
##      9        1.2336             nan     0.1000    0.0042
##     10        1.2237             nan     0.1000    0.0036
##     20        1.1577             nan     0.1000    0.0040
##     40        1.0700             nan     0.1000    0.0012
##     60        1.0158             nan     0.1000    0.0007
##     80        0.9744             nan     0.1000    0.0018
##    100        0.9348             nan     0.1000    0.0003
##    120        0.9040             nan     0.1000   -0.0002
##    140        0.8689             nan     0.1000   -0.0000
##    160        0.8431             nan     0.1000   -0.0008
##    180        0.8210             nan     0.1000    0.0003
##    200        0.8011             nan     0.1000   -0.0003
##    220        0.7813             nan     0.1000    0.0001
##    240        0.7607             nan     0.1000   -0.0004
##    260        0.7432             nan     0.1000    0.0001
##    280        0.7248             nan     0.1000   -0.0007
##    300        0.7086             nan     0.1000   -0.0001
##    320        0.6949             nan     0.1000   -0.0002
##    340        0.6823             nan     0.1000   -0.0004
##    360        0.6660             nan     0.1000   -0.0004
##    380        0.6552             nan     0.1000   -0.0008
##    400        0.6437             nan     0.1000   -0.0002
##    420        0.6321             nan     0.1000   -0.0003
##    440        0.6216             nan     0.1000   -0.0004
##    460        0.6102             nan     0.1000   -0.0003
##    480        0.5996             nan     0.1000   -0.0005
##    500        0.5902             nan     0.1000   -0.0003
## 
## - Fold06: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3422             nan     0.1000    0.0113
##      2        1.3179             nan     0.1000    0.0099
##      3        1.2998             nan     0.1000    0.0083
##      4        1.2828             nan     0.1000    0.0068
##      5        1.2671             nan     0.1000    0.0067
##      6        1.2526             nan     0.1000    0.0058
##      7        1.2373             nan     0.1000    0.0075
##      8        1.2268             nan     0.1000    0.0041
##      9        1.2187             nan     0.1000    0.0024
##     10        1.2085             nan     0.1000    0.0043
##     20        1.1261             nan     0.1000    0.0012
##     40        1.0238             nan     0.1000    0.0017
##     60        0.9592             nan     0.1000    0.0013
##     80        0.9131             nan     0.1000    0.0003
##    100        0.8691             nan     0.1000    0.0005
##    120        0.8297             nan     0.1000   -0.0001
##    140        0.7967             nan     0.1000   -0.0003
##    160        0.7606             nan     0.1000    0.0004
##    180        0.7318             nan     0.1000    0.0003
##    200        0.7083             nan     0.1000   -0.0003
##    220        0.6873             nan     0.1000   -0.0002
##    240        0.6656             nan     0.1000    0.0001
##    260        0.6456             nan     0.1000   -0.0003
##    280        0.6274             nan     0.1000    0.0000
##    300        0.6087             nan     0.1000   -0.0004
##    320        0.5925             nan     0.1000   -0.0001
##    340        0.5766             nan     0.1000   -0.0001
##    360        0.5596             nan     0.1000    0.0002
##    380        0.5445             nan     0.1000   -0.0002
##    400        0.5301             nan     0.1000   -0.0004
##    420        0.5174             nan     0.1000   -0.0002
##    440        0.5060             nan     0.1000   -0.0003
##    460        0.4929             nan     0.1000   -0.0004
##    480        0.4817             nan     0.1000   -0.0002
##    500        0.4700             nan     0.1000   -0.0004
## 
## - Fold06: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3356             nan     0.1000    0.0149
##      2        1.3109             nan     0.1000    0.0106
##      3        1.2887             nan     0.1000    0.0108
##      4        1.2681             nan     0.1000    0.0087
##      5        1.2533             nan     0.1000    0.0063
##      6        1.2345             nan     0.1000    0.0093
##      7        1.2190             nan     0.1000    0.0069
##      8        1.2072             nan     0.1000    0.0046
##      9        1.1923             nan     0.1000    0.0068
##     10        1.1813             nan     0.1000    0.0045
##     20        1.0919             nan     0.1000    0.0018
##     40        0.9871             nan     0.1000    0.0007
##     60        0.9174             nan     0.1000    0.0002
##     80        0.8541             nan     0.1000    0.0011
##    100        0.7995             nan     0.1000    0.0006
##    120        0.7570             nan     0.1000   -0.0005
##    140        0.7242             nan     0.1000   -0.0002
##    160        0.6885             nan     0.1000   -0.0000
##    180        0.6631             nan     0.1000   -0.0003
##    200        0.6363             nan     0.1000   -0.0002
##    220        0.6116             nan     0.1000    0.0002
##    240        0.5890             nan     0.1000   -0.0001
##    260        0.5681             nan     0.1000   -0.0002
##    280        0.5499             nan     0.1000   -0.0000
##    300        0.5327             nan     0.1000   -0.0005
##    320        0.5169             nan     0.1000   -0.0001
##    340        0.4990             nan     0.1000   -0.0002
##    360        0.4833             nan     0.1000    0.0001
##    380        0.4691             nan     0.1000   -0.0002
##    400        0.4542             nan     0.1000   -0.0000
##    420        0.4407             nan     0.1000   -0.0002
##    440        0.4268             nan     0.1000   -0.0005
##    460        0.4142             nan     0.1000   -0.0005
##    480        0.4023             nan     0.1000   -0.0003
##    500        0.3912             nan     0.1000   -0.0005
## 
## - Fold06: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3386             nan     0.1000    0.0124
##      2        1.3142             nan     0.1000    0.0111
##      3        1.2910             nan     0.1000    0.0100
##      4        1.2664             nan     0.1000    0.0113
##      5        1.2469             nan     0.1000    0.0073
##      6        1.2287             nan     0.1000    0.0071
##      7        1.2165             nan     0.1000    0.0046
##      8        1.2034             nan     0.1000    0.0045
##      9        1.1882             nan     0.1000    0.0064
##     10        1.1756             nan     0.1000    0.0057
##     20        1.0776             nan     0.1000    0.0034
##     40        0.9576             nan     0.1000    0.0011
##     60        0.8769             nan     0.1000    0.0003
##     80        0.8187             nan     0.1000   -0.0002
##    100        0.7630             nan     0.1000   -0.0005
##    120        0.7148             nan     0.1000   -0.0002
##    140        0.6770             nan     0.1000    0.0000
##    160        0.6484             nan     0.1000   -0.0002
##    180        0.6163             nan     0.1000   -0.0001
##    200        0.5871             nan     0.1000   -0.0001
##    220        0.5635             nan     0.1000   -0.0005
##    240        0.5402             nan     0.1000   -0.0004
##    260        0.5171             nan     0.1000   -0.0003
##    280        0.4970             nan     0.1000    0.0002
##    300        0.4762             nan     0.1000   -0.0003
##    320        0.4579             nan     0.1000   -0.0002
##    340        0.4423             nan     0.1000   -0.0003
##    360        0.4247             nan     0.1000   -0.0002
##    380        0.4080             nan     0.1000   -0.0004
##    400        0.3934             nan     0.1000   -0.0004
##    420        0.3791             nan     0.1000   -0.0002
##    440        0.3657             nan     0.1000   -0.0004
##    460        0.3533             nan     0.1000   -0.0002
##    480        0.3422             nan     0.1000   -0.0004
##    500        0.3304             nan     0.1000   -0.0002
## 
## - Fold06: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3290             nan     0.1000    0.0172
##      2        1.2975             nan     0.1000    0.0135
##      3        1.2753             nan     0.1000    0.0092
##      4        1.2560             nan     0.1000    0.0081
##      5        1.2348             nan     0.1000    0.0088
##      6        1.2148             nan     0.1000    0.0092
##      7        1.1972             nan     0.1000    0.0075
##      8        1.1830             nan     0.1000    0.0053
##      9        1.1705             nan     0.1000    0.0045
##     10        1.1560             nan     0.1000    0.0062
##     20        1.0547             nan     0.1000    0.0023
##     40        0.9273             nan     0.1000    0.0000
##     60        0.8439             nan     0.1000   -0.0006
##     80        0.7771             nan     0.1000    0.0002
##    100        0.7259             nan     0.1000    0.0001
##    120        0.6824             nan     0.1000   -0.0003
##    140        0.6431             nan     0.1000   -0.0000
##    160        0.6109             nan     0.1000    0.0000
##    180        0.5778             nan     0.1000   -0.0002
##    200        0.5486             nan     0.1000   -0.0002
##    220        0.5205             nan     0.1000   -0.0006
##    240        0.4957             nan     0.1000   -0.0002
##    260        0.4719             nan     0.1000    0.0000
##    280        0.4491             nan     0.1000   -0.0002
##    300        0.4300             nan     0.1000   -0.0005
##    320        0.4112             nan     0.1000   -0.0001
##    340        0.3955             nan     0.1000   -0.0004
##    360        0.3783             nan     0.1000   -0.0004
##    380        0.3615             nan     0.1000   -0.0002
##    400        0.3468             nan     0.1000   -0.0001
##    420        0.3334             nan     0.1000   -0.0002
##    440        0.3208             nan     0.1000   -0.0003
##    460        0.3071             nan     0.1000   -0.0003
##    480        0.2944             nan     0.1000   -0.0002
##    500        0.2823             nan     0.1000    0.0000
## 
## - Fold06: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3329             nan     0.1000    0.0146
##      2        1.3055             nan     0.1000    0.0121
##      3        1.2813             nan     0.1000    0.0104
##      4        1.2567             nan     0.1000    0.0100
##      5        1.2344             nan     0.1000    0.0100
##      6        1.2144             nan     0.1000    0.0073
##      7        1.1970             nan     0.1000    0.0062
##      8        1.1791             nan     0.1000    0.0077
##      9        1.1626             nan     0.1000    0.0068
##     10        1.1468             nan     0.1000    0.0062
##     20        1.0385             nan     0.1000    0.0023
##     40        0.9030             nan     0.1000    0.0002
##     60        0.8085             nan     0.1000    0.0005
##     80        0.7466             nan     0.1000   -0.0003
##    100        0.6904             nan     0.1000   -0.0001
##    120        0.6385             nan     0.1000   -0.0000
##    140        0.5936             nan     0.1000    0.0003
##    160        0.5592             nan     0.1000   -0.0003
##    180        0.5247             nan     0.1000   -0.0008
##    200        0.4940             nan     0.1000   -0.0002
##    220        0.4655             nan     0.1000   -0.0002
##    240        0.4423             nan     0.1000   -0.0003
##    260        0.4201             nan     0.1000   -0.0010
##    280        0.3987             nan     0.1000   -0.0006
##    300        0.3769             nan     0.1000   -0.0001
##    320        0.3593             nan     0.1000   -0.0005
##    340        0.3407             nan     0.1000   -0.0003
##    360        0.3243             nan     0.1000   -0.0002
##    380        0.3102             nan     0.1000   -0.0002
##    400        0.2951             nan     0.1000   -0.0004
##    420        0.2822             nan     0.1000   -0.0000
##    440        0.2698             nan     0.1000   -0.0003
##    460        0.2565             nan     0.1000   -0.0002
##    480        0.2460             nan     0.1000   -0.0004
##    500        0.2357             nan     0.1000   -0.0002
## 
## - Fold06: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3289             nan     0.1000    0.0174
##      2        1.2967             nan     0.1000    0.0123
##      3        1.2702             nan     0.1000    0.0107
##      4        1.2438             nan     0.1000    0.0119
##      5        1.2191             nan     0.1000    0.0103
##      6        1.1959             nan     0.1000    0.0097
##      7        1.1803             nan     0.1000    0.0052
##      8        1.1637             nan     0.1000    0.0061
##      9        1.1469             nan     0.1000    0.0063
##     10        1.1320             nan     0.1000    0.0060
##     20        1.0225             nan     0.1000    0.0007
##     40        0.8785             nan     0.1000    0.0007
##     60        0.7833             nan     0.1000    0.0004
##     80        0.7074             nan     0.1000    0.0002
##    100        0.6458             nan     0.1000   -0.0002
##    120        0.5940             nan     0.1000    0.0005
##    140        0.5520             nan     0.1000    0.0000
##    160        0.5177             nan     0.1000   -0.0006
##    180        0.4817             nan     0.1000   -0.0005
##    200        0.4512             nan     0.1000   -0.0003
##    220        0.4231             nan     0.1000   -0.0003
##    240        0.4009             nan     0.1000   -0.0005
##    260        0.3774             nan     0.1000   -0.0004
##    280        0.3564             nan     0.1000   -0.0004
##    300        0.3363             nan     0.1000   -0.0006
##    320        0.3188             nan     0.1000   -0.0005
##    340        0.3023             nan     0.1000   -0.0004
##    360        0.2864             nan     0.1000   -0.0004
##    380        0.2701             nan     0.1000   -0.0002
##    400        0.2562             nan     0.1000   -0.0002
##    420        0.2434             nan     0.1000   -0.0002
##    440        0.2297             nan     0.1000   -0.0001
##    460        0.2176             nan     0.1000   -0.0004
##    480        0.2053             nan     0.1000   -0.0002
##    500        0.1953             nan     0.1000   -0.0001
## 
## - Fold06: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold06: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3252             nan     0.1000    0.0181
##      2        1.2920             nan     0.1000    0.0148
##      3        1.2616             nan     0.1000    0.0127
##      4        1.2332             nan     0.1000    0.0109
##      5        1.2097             nan     0.1000    0.0099
##      6        1.1860             nan     0.1000    0.0095
##      7        1.1668             nan     0.1000    0.0069
##      8        1.1523             nan     0.1000    0.0046
##      9        1.1361             nan     0.1000    0.0061
##     10        1.1220             nan     0.1000    0.0038
##     20        1.0015             nan     0.1000    0.0064
##     40        0.8442             nan     0.1000    0.0013
##     60        0.7467             nan     0.1000    0.0000
##     80        0.6727             nan     0.1000    0.0000
##    100        0.6156             nan     0.1000   -0.0005
##    120        0.5673             nan     0.1000    0.0000
##    140        0.5232             nan     0.1000   -0.0002
##    160        0.4855             nan     0.1000    0.0001
##    180        0.4498             nan     0.1000    0.0000
##    200        0.4193             nan     0.1000   -0.0003
##    220        0.3905             nan     0.1000   -0.0003
##    240        0.3665             nan     0.1000   -0.0001
##    260        0.3426             nan     0.1000   -0.0003
##    280        0.3206             nan     0.1000   -0.0005
##    300        0.2996             nan     0.1000   -0.0003
##    320        0.2806             nan     0.1000   -0.0002
##    340        0.2634             nan     0.1000   -0.0003
##    360        0.2483             nan     0.1000   -0.0002
##    380        0.2337             nan     0.1000   -0.0005
##    400        0.2199             nan     0.1000   -0.0003
##    420        0.2076             nan     0.1000   -0.0001
##    440        0.1953             nan     0.1000   -0.0002
##    460        0.1845             nan     0.1000   -0.0002
##    480        0.1746             nan     0.1000   -0.0005
##    500        0.1651             nan     0.1000   -0.0002
## 
## - Fold06: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3532             nan     0.1000    0.0064
##      2        1.3429             nan     0.1000    0.0056
##      3        1.3330             nan     0.1000    0.0043
##      4        1.3231             nan     0.1000    0.0054
##      5        1.3154             nan     0.1000    0.0038
##      6        1.3072             nan     0.1000    0.0039
##      7        1.3011             nan     0.1000    0.0030
##      8        1.2939             nan     0.1000    0.0029
##      9        1.2877             nan     0.1000    0.0024
##     10        1.2826             nan     0.1000    0.0024
##     20        1.2424             nan     0.1000    0.0015
##     40        1.1968             nan     0.1000    0.0006
##     60        1.1698             nan     0.1000   -0.0001
##     80        1.1500             nan     0.1000    0.0001
##    100        1.1375             nan     0.1000   -0.0003
##    120        1.1247             nan     0.1000   -0.0003
##    140        1.1135             nan     0.1000   -0.0002
##    160        1.1024             nan     0.1000    0.0000
##    180        1.0930             nan     0.1000   -0.0002
##    200        1.0858             nan     0.1000   -0.0001
##    220        1.0784             nan     0.1000    0.0000
##    240        1.0714             nan     0.1000   -0.0000
##    260        1.0644             nan     0.1000   -0.0000
##    280        1.0575             nan     0.1000   -0.0002
##    300        1.0502             nan     0.1000   -0.0001
##    320        1.0439             nan     0.1000    0.0000
##    340        1.0385             nan     0.1000   -0.0004
##    360        1.0338             nan     0.1000   -0.0000
##    380        1.0276             nan     0.1000   -0.0001
##    400        1.0218             nan     0.1000   -0.0003
##    420        1.0172             nan     0.1000   -0.0001
##    440        1.0119             nan     0.1000   -0.0003
##    460        1.0073             nan     0.1000   -0.0006
##    480        1.0027             nan     0.1000   -0.0003
##    500        0.9980             nan     0.1000   -0.0006
## 
## - Fold07: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3451             nan     0.1000    0.0102
##      2        1.3256             nan     0.1000    0.0092
##      3        1.3121             nan     0.1000    0.0058
##      4        1.2977             nan     0.1000    0.0072
##      5        1.2850             nan     0.1000    0.0058
##      6        1.2762             nan     0.1000    0.0036
##      7        1.2660             nan     0.1000    0.0049
##      8        1.2566             nan     0.1000    0.0037
##      9        1.2474             nan     0.1000    0.0038
##     10        1.2369             nan     0.1000    0.0051
##     20        1.1715             nan     0.1000    0.0025
##     40        1.1003             nan     0.1000    0.0002
##     60        1.0538             nan     0.1000    0.0005
##     80        1.0118             nan     0.1000   -0.0000
##    100        0.9863             nan     0.1000   -0.0001
##    120        0.9572             nan     0.1000   -0.0003
##    140        0.9328             nan     0.1000   -0.0002
##    160        0.9103             nan     0.1000    0.0004
##    180        0.8872             nan     0.1000    0.0002
##    200        0.8691             nan     0.1000   -0.0002
##    220        0.8526             nan     0.1000   -0.0003
##    240        0.8382             nan     0.1000    0.0001
##    260        0.8186             nan     0.1000   -0.0005
##    280        0.8042             nan     0.1000   -0.0000
##    300        0.7869             nan     0.1000   -0.0001
##    320        0.7765             nan     0.1000   -0.0009
##    340        0.7646             nan     0.1000   -0.0007
##    360        0.7541             nan     0.1000    0.0001
##    380        0.7436             nan     0.1000   -0.0001
##    400        0.7333             nan     0.1000   -0.0001
##    420        0.7237             nan     0.1000   -0.0004
##    440        0.7144             nan     0.1000   -0.0002
##    460        0.7048             nan     0.1000   -0.0003
##    480        0.6974             nan     0.1000   -0.0002
##    500        0.6887             nan     0.1000   -0.0003
## 
## - Fold07: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3416             nan     0.1000    0.0116
##      2        1.3213             nan     0.1000    0.0085
##      3        1.3023             nan     0.1000    0.0088
##      4        1.2884             nan     0.1000    0.0063
##      5        1.2730             nan     0.1000    0.0072
##      6        1.2595             nan     0.1000    0.0063
##      7        1.2454             nan     0.1000    0.0062
##      8        1.2368             nan     0.1000    0.0035
##      9        1.2254             nan     0.1000    0.0050
##     10        1.2157             nan     0.1000    0.0041
##     20        1.1499             nan     0.1000    0.0013
##     40        1.0579             nan     0.1000    0.0007
##     60        0.9999             nan     0.1000    0.0013
##     80        0.9595             nan     0.1000    0.0006
##    100        0.9228             nan     0.1000    0.0004
##    120        0.8870             nan     0.1000   -0.0000
##    140        0.8559             nan     0.1000   -0.0002
##    160        0.8253             nan     0.1000   -0.0001
##    180        0.7981             nan     0.1000   -0.0002
##    200        0.7734             nan     0.1000    0.0007
##    220        0.7551             nan     0.1000   -0.0006
##    240        0.7353             nan     0.1000   -0.0002
##    260        0.7174             nan     0.1000   -0.0001
##    280        0.7029             nan     0.1000   -0.0002
##    300        0.6832             nan     0.1000   -0.0002
##    320        0.6675             nan     0.1000   -0.0001
##    340        0.6558             nan     0.1000   -0.0003
##    360        0.6431             nan     0.1000   -0.0001
##    380        0.6289             nan     0.1000    0.0002
##    400        0.6163             nan     0.1000   -0.0004
##    420        0.6052             nan     0.1000   -0.0003
##    440        0.5938             nan     0.1000   -0.0002
##    460        0.5808             nan     0.1000   -0.0002
##    480        0.5713             nan     0.1000   -0.0006
##    500        0.5606             nan     0.1000   -0.0004
## 
## - Fold07: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3386             nan     0.1000    0.0116
##      2        1.3117             nan     0.1000    0.0125
##      3        1.2932             nan     0.1000    0.0087
##      4        1.2718             nan     0.1000    0.0103
##      5        1.2543             nan     0.1000    0.0075
##      6        1.2381             nan     0.1000    0.0075
##      7        1.2237             nan     0.1000    0.0064
##      8        1.2096             nan     0.1000    0.0058
##      9        1.1984             nan     0.1000    0.0042
##     10        1.1864             nan     0.1000    0.0053
##     20        1.1050             nan     0.1000    0.0029
##     40        1.0024             nan     0.1000    0.0022
##     60        0.9350             nan     0.1000    0.0001
##     80        0.8906             nan     0.1000    0.0004
##    100        0.8468             nan     0.1000    0.0007
##    120        0.8079             nan     0.1000    0.0008
##    140        0.7702             nan     0.1000    0.0002
##    160        0.7430             nan     0.1000   -0.0002
##    180        0.7174             nan     0.1000   -0.0004
##    200        0.6892             nan     0.1000    0.0005
##    220        0.6696             nan     0.1000   -0.0005
##    240        0.6466             nan     0.1000   -0.0001
##    260        0.6251             nan     0.1000   -0.0003
##    280        0.6050             nan     0.1000   -0.0001
##    300        0.5879             nan     0.1000    0.0002
##    320        0.5696             nan     0.1000   -0.0004
##    340        0.5534             nan     0.1000   -0.0006
##    360        0.5384             nan     0.1000   -0.0003
##    380        0.5234             nan     0.1000   -0.0001
##    400        0.5107             nan     0.1000   -0.0002
##    420        0.4967             nan     0.1000   -0.0004
##    440        0.4846             nan     0.1000   -0.0002
##    460        0.4713             nan     0.1000   -0.0002
##    480        0.4608             nan     0.1000   -0.0003
##    500        0.4496             nan     0.1000   -0.0003
## 
## - Fold07: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3351             nan     0.1000    0.0136
##      2        1.3098             nan     0.1000    0.0114
##      3        1.2858             nan     0.1000    0.0113
##      4        1.2639             nan     0.1000    0.0100
##      5        1.2456             nan     0.1000    0.0080
##      6        1.2284             nan     0.1000    0.0078
##      7        1.2127             nan     0.1000    0.0070
##      8        1.1966             nan     0.1000    0.0071
##      9        1.1839             nan     0.1000    0.0050
##     10        1.1687             nan     0.1000    0.0062
##     20        1.0827             nan     0.1000    0.0040
##     40        0.9773             nan     0.1000    0.0020
##     60        0.9010             nan     0.1000    0.0001
##     80        0.8452             nan     0.1000    0.0014
##    100        0.7972             nan     0.1000    0.0001
##    120        0.7591             nan     0.1000    0.0003
##    140        0.7227             nan     0.1000    0.0004
##    160        0.6931             nan     0.1000   -0.0002
##    180        0.6606             nan     0.1000    0.0003
##    200        0.6327             nan     0.1000   -0.0000
##    220        0.6062             nan     0.1000   -0.0003
##    240        0.5854             nan     0.1000   -0.0006
##    260        0.5615             nan     0.1000    0.0000
##    280        0.5439             nan     0.1000   -0.0006
##    300        0.5250             nan     0.1000   -0.0001
##    320        0.5083             nan     0.1000   -0.0004
##    340        0.4897             nan     0.1000   -0.0000
##    360        0.4727             nan     0.1000   -0.0003
##    380        0.4568             nan     0.1000   -0.0005
##    400        0.4433             nan     0.1000   -0.0004
##    420        0.4307             nan     0.1000   -0.0006
##    440        0.4177             nan     0.1000   -0.0004
##    460        0.4072             nan     0.1000   -0.0003
##    480        0.3951             nan     0.1000   -0.0004
##    500        0.3846             nan     0.1000   -0.0002
## 
## - Fold07: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3329             nan     0.1000    0.0158
##      2        1.3032             nan     0.1000    0.0148
##      3        1.2783             nan     0.1000    0.0106
##      4        1.2572             nan     0.1000    0.0093
##      5        1.2351             nan     0.1000    0.0093
##      6        1.2186             nan     0.1000    0.0074
##      7        1.2014             nan     0.1000    0.0078
##      8        1.1864             nan     0.1000    0.0054
##      9        1.1734             nan     0.1000    0.0045
##     10        1.1621             nan     0.1000    0.0040
##     20        1.0698             nan     0.1000    0.0014
##     40        0.9459             nan     0.1000    0.0021
##     60        0.8722             nan     0.1000    0.0005
##     80        0.8030             nan     0.1000   -0.0005
##    100        0.7459             nan     0.1000    0.0008
##    120        0.7038             nan     0.1000   -0.0005
##    140        0.6657             nan     0.1000   -0.0006
##    160        0.6318             nan     0.1000   -0.0003
##    180        0.6003             nan     0.1000   -0.0004
##    200        0.5755             nan     0.1000   -0.0001
##    220        0.5500             nan     0.1000   -0.0002
##    240        0.5282             nan     0.1000   -0.0002
##    260        0.5072             nan     0.1000   -0.0002
##    280        0.4851             nan     0.1000   -0.0002
##    300        0.4631             nan     0.1000   -0.0003
##    320        0.4444             nan     0.1000   -0.0001
##    340        0.4263             nan     0.1000   -0.0003
##    360        0.4107             nan     0.1000   -0.0003
##    380        0.3969             nan     0.1000   -0.0006
##    400        0.3817             nan     0.1000   -0.0004
##    420        0.3680             nan     0.1000   -0.0003
##    440        0.3541             nan     0.1000    0.0002
##    460        0.3420             nan     0.1000   -0.0001
##    480        0.3296             nan     0.1000   -0.0002
##    500        0.3175             nan     0.1000   -0.0005
## 
## - Fold07: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3322             nan     0.1000    0.0149
##      2        1.3050             nan     0.1000    0.0125
##      3        1.2780             nan     0.1000    0.0116
##      4        1.2525             nan     0.1000    0.0130
##      5        1.2303             nan     0.1000    0.0093
##      6        1.2110             nan     0.1000    0.0079
##      7        1.1954             nan     0.1000    0.0056
##      8        1.1784             nan     0.1000    0.0069
##      9        1.1642             nan     0.1000    0.0046
##     10        1.1503             nan     0.1000    0.0065
##     20        1.0450             nan     0.1000    0.0038
##     40        0.9286             nan     0.1000    0.0001
##     60        0.8417             nan     0.1000    0.0017
##     80        0.7785             nan     0.1000   -0.0002
##    100        0.7178             nan     0.1000   -0.0005
##    120        0.6726             nan     0.1000   -0.0001
##    140        0.6298             nan     0.1000   -0.0002
##    160        0.5908             nan     0.1000    0.0006
##    180        0.5589             nan     0.1000    0.0002
##    200        0.5282             nan     0.1000   -0.0004
##    220        0.4986             nan     0.1000   -0.0002
##    240        0.4731             nan     0.1000   -0.0002
##    260        0.4490             nan     0.1000   -0.0002
##    280        0.4265             nan     0.1000   -0.0002
##    300        0.4063             nan     0.1000    0.0001
##    320        0.3863             nan     0.1000   -0.0003
##    340        0.3678             nan     0.1000   -0.0000
##    360        0.3514             nan     0.1000   -0.0004
##    380        0.3352             nan     0.1000   -0.0004
##    400        0.3203             nan     0.1000   -0.0004
##    420        0.3066             nan     0.1000   -0.0001
##    440        0.2931             nan     0.1000   -0.0005
##    460        0.2801             nan     0.1000   -0.0004
##    480        0.2686             nan     0.1000   -0.0001
##    500        0.2575             nan     0.1000   -0.0003
## 
## - Fold07: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3257             nan     0.1000    0.0198
##      2        1.2942             nan     0.1000    0.0150
##      3        1.2639             nan     0.1000    0.0132
##      4        1.2398             nan     0.1000    0.0099
##      5        1.2195             nan     0.1000    0.0084
##      6        1.1981             nan     0.1000    0.0086
##      7        1.1800             nan     0.1000    0.0070
##      8        1.1648             nan     0.1000    0.0062
##      9        1.1487             nan     0.1000    0.0051
##     10        1.1359             nan     0.1000    0.0041
##     20        1.0197             nan     0.1000    0.0037
##     40        0.8879             nan     0.1000   -0.0004
##     60        0.7979             nan     0.1000    0.0009
##     80        0.7324             nan     0.1000   -0.0002
##    100        0.6803             nan     0.1000    0.0002
##    120        0.6254             nan     0.1000    0.0005
##    140        0.5828             nan     0.1000   -0.0002
##    160        0.5432             nan     0.1000    0.0006
##    180        0.5129             nan     0.1000   -0.0000
##    200        0.4837             nan     0.1000   -0.0004
##    220        0.4568             nan     0.1000   -0.0000
##    240        0.4313             nan     0.1000   -0.0007
##    260        0.4081             nan     0.1000   -0.0001
##    280        0.3870             nan     0.1000   -0.0002
##    300        0.3654             nan     0.1000   -0.0005
##    320        0.3462             nan     0.1000   -0.0005
##    340        0.3279             nan     0.1000   -0.0002
##    360        0.3118             nan     0.1000   -0.0005
##    380        0.2963             nan     0.1000   -0.0006
##    400        0.2820             nan     0.1000   -0.0003
##    420        0.2680             nan     0.1000   -0.0003
##    440        0.2538             nan     0.1000   -0.0000
##    460        0.2424             nan     0.1000   -0.0002
##    480        0.2303             nan     0.1000   -0.0002
##    500        0.2201             nan     0.1000   -0.0003
## 
## - Fold07: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3256             nan     0.1000    0.0173
##      2        1.2978             nan     0.1000    0.0109
##      3        1.2689             nan     0.1000    0.0135
##      4        1.2442             nan     0.1000    0.0107
##      5        1.2189             nan     0.1000    0.0110
##      6        1.1991             nan     0.1000    0.0068
##      7        1.1814             nan     0.1000    0.0072
##      8        1.1640             nan     0.1000    0.0076
##      9        1.1508             nan     0.1000    0.0037
##     10        1.1375             nan     0.1000    0.0040
##     20        1.0272             nan     0.1000    0.0015
##     40        0.8784             nan     0.1000    0.0015
##     60        0.7767             nan     0.1000    0.0022
##     80        0.7023             nan     0.1000   -0.0015
##    100        0.6424             nan     0.1000   -0.0001
##    120        0.5906             nan     0.1000    0.0006
##    140        0.5475             nan     0.1000   -0.0002
##    160        0.5108             nan     0.1000   -0.0005
##    180        0.4748             nan     0.1000   -0.0004
##    200        0.4456             nan     0.1000   -0.0002
##    220        0.4186             nan     0.1000    0.0001
##    240        0.3929             nan     0.1000   -0.0005
##    260        0.3702             nan     0.1000   -0.0004
##    280        0.3481             nan     0.1000   -0.0004
##    300        0.3280             nan     0.1000    0.0001
##    320        0.3114             nan     0.1000   -0.0006
##    340        0.2954             nan     0.1000   -0.0002
##    360        0.2792             nan     0.1000   -0.0003
##    380        0.2637             nan     0.1000   -0.0002
##    400        0.2499             nan     0.1000   -0.0004
##    420        0.2357             nan     0.1000   -0.0002
##    440        0.2242             nan     0.1000   -0.0003
##    460        0.2127             nan     0.1000   -0.0004
##    480        0.2018             nan     0.1000   -0.0001
##    500        0.1914             nan     0.1000   -0.0003
## 
## - Fold07: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold07: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3289             nan     0.1000    0.0157
##      2        1.2984             nan     0.1000    0.0132
##      3        1.2697             nan     0.1000    0.0111
##      4        1.2456             nan     0.1000    0.0100
##      5        1.2242             nan     0.1000    0.0083
##      6        1.2055             nan     0.1000    0.0069
##      7        1.1829             nan     0.1000    0.0097
##      8        1.1650             nan     0.1000    0.0071
##      9        1.1448             nan     0.1000    0.0084
##     10        1.1317             nan     0.1000    0.0030
##     20        1.0043             nan     0.1000    0.0036
##     40        0.8523             nan     0.1000    0.0004
##     60        0.7524             nan     0.1000    0.0015
##     80        0.6769             nan     0.1000    0.0003
##    100        0.6158             nan     0.1000   -0.0003
##    120        0.5678             nan     0.1000   -0.0001
##    140        0.5191             nan     0.1000    0.0001
##    160        0.4790             nan     0.1000   -0.0000
##    180        0.4429             nan     0.1000   -0.0004
##    200        0.4118             nan     0.1000   -0.0003
##    220        0.3859             nan     0.1000   -0.0003
##    240        0.3584             nan     0.1000   -0.0004
##    260        0.3348             nan     0.1000   -0.0005
##    280        0.3131             nan     0.1000    0.0002
##    300        0.2929             nan     0.1000   -0.0001
##    320        0.2742             nan     0.1000   -0.0002
##    340        0.2573             nan     0.1000   -0.0002
##    360        0.2410             nan     0.1000   -0.0004
##    380        0.2272             nan     0.1000   -0.0003
##    400        0.2147             nan     0.1000   -0.0003
##    420        0.2014             nan     0.1000   -0.0001
##    440        0.1887             nan     0.1000   -0.0002
##    460        0.1775             nan     0.1000   -0.0002
##    480        0.1664             nan     0.1000   -0.0001
##    500        0.1584             nan     0.1000   -0.0002
## 
## - Fold07: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3494             nan     0.1000    0.0076
##      2        1.3353             nan     0.1000    0.0066
##      3        1.3246             nan     0.1000    0.0053
##      4        1.3136             nan     0.1000    0.0043
##      5        1.3034             nan     0.1000    0.0043
##      6        1.2949             nan     0.1000    0.0030
##      7        1.2871             nan     0.1000    0.0034
##      8        1.2800             nan     0.1000    0.0031
##      9        1.2748             nan     0.1000    0.0022
##     10        1.2686             nan     0.1000    0.0028
##     20        1.2266             nan     0.1000    0.0014
##     40        1.1849             nan     0.1000    0.0003
##     60        1.1583             nan     0.1000    0.0002
##     80        1.1387             nan     0.1000   -0.0003
##    100        1.1226             nan     0.1000   -0.0002
##    120        1.1089             nan     0.1000    0.0001
##    140        1.0971             nan     0.1000    0.0000
##    160        1.0870             nan     0.1000   -0.0002
##    180        1.0773             nan     0.1000    0.0000
##    200        1.0691             nan     0.1000   -0.0002
##    220        1.0594             nan     0.1000   -0.0001
##    240        1.0513             nan     0.1000   -0.0002
##    260        1.0446             nan     0.1000   -0.0001
##    280        1.0365             nan     0.1000   -0.0003
##    300        1.0296             nan     0.1000   -0.0001
##    320        1.0236             nan     0.1000   -0.0001
##    340        1.0168             nan     0.1000    0.0001
##    360        1.0105             nan     0.1000   -0.0002
##    380        1.0053             nan     0.1000   -0.0004
##    400        1.0000             nan     0.1000   -0.0000
##    420        0.9938             nan     0.1000   -0.0004
##    440        0.9888             nan     0.1000   -0.0002
##    460        0.9846             nan     0.1000   -0.0003
##    480        0.9792             nan     0.1000   -0.0003
##    500        0.9738             nan     0.1000   -0.0001
## 
## - Fold08: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3454             nan     0.1000    0.0102
##      2        1.3294             nan     0.1000    0.0079
##      3        1.3122             nan     0.1000    0.0083
##      4        1.2990             nan     0.1000    0.0064
##      5        1.2855             nan     0.1000    0.0064
##      6        1.2743             nan     0.1000    0.0054
##      7        1.2634             nan     0.1000    0.0042
##      8        1.2556             nan     0.1000    0.0037
##      9        1.2472             nan     0.1000    0.0032
##     10        1.2392             nan     0.1000    0.0027
##     20        1.1768             nan     0.1000    0.0017
##     40        1.1021             nan     0.1000    0.0008
##     60        1.0612             nan     0.1000    0.0002
##     80        1.0234             nan     0.1000    0.0003
##    100        0.9909             nan     0.1000   -0.0002
##    120        0.9648             nan     0.1000   -0.0001
##    140        0.9373             nan     0.1000   -0.0001
##    160        0.9125             nan     0.1000   -0.0008
##    180        0.8923             nan     0.1000    0.0007
##    200        0.8743             nan     0.1000   -0.0004
##    220        0.8574             nan     0.1000   -0.0004
##    240        0.8425             nan     0.1000   -0.0004
##    260        0.8314             nan     0.1000    0.0000
##    280        0.8168             nan     0.1000    0.0000
##    300        0.8037             nan     0.1000   -0.0001
##    320        0.7923             nan     0.1000   -0.0003
##    340        0.7823             nan     0.1000   -0.0003
##    360        0.7719             nan     0.1000   -0.0003
##    380        0.7629             nan     0.1000   -0.0004
##    400        0.7530             nan     0.1000   -0.0003
##    420        0.7426             nan     0.1000    0.0001
##    440        0.7326             nan     0.1000   -0.0001
##    460        0.7269             nan     0.1000   -0.0002
##    480        0.7187             nan     0.1000   -0.0002
##    500        0.7086             nan     0.1000   -0.0001
## 
## - Fold08: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3383             nan     0.1000    0.0128
##      2        1.3163             nan     0.1000    0.0104
##      3        1.3013             nan     0.1000    0.0066
##      4        1.2830             nan     0.1000    0.0085
##      5        1.2666             nan     0.1000    0.0064
##      6        1.2524             nan     0.1000    0.0060
##      7        1.2391             nan     0.1000    0.0054
##      8        1.2291             nan     0.1000    0.0040
##      9        1.2179             nan     0.1000    0.0055
##     10        1.2087             nan     0.1000    0.0026
##     20        1.1371             nan     0.1000    0.0024
##     40        1.0617             nan     0.1000    0.0006
##     60        0.9948             nan     0.1000    0.0011
##     80        0.9491             nan     0.1000    0.0009
##    100        0.9183             nan     0.1000   -0.0002
##    120        0.8841             nan     0.1000    0.0010
##    140        0.8568             nan     0.1000    0.0005
##    160        0.8313             nan     0.1000    0.0002
##    180        0.8055             nan     0.1000   -0.0004
##    200        0.7817             nan     0.1000   -0.0004
##    220        0.7620             nan     0.1000   -0.0005
##    240        0.7432             nan     0.1000   -0.0006
##    260        0.7247             nan     0.1000   -0.0001
##    280        0.7093             nan     0.1000   -0.0003
##    300        0.6942             nan     0.1000   -0.0003
##    320        0.6800             nan     0.1000   -0.0001
##    340        0.6649             nan     0.1000    0.0002
##    360        0.6510             nan     0.1000   -0.0003
##    380        0.6398             nan     0.1000   -0.0002
##    400        0.6286             nan     0.1000   -0.0000
##    420        0.6166             nan     0.1000   -0.0002
##    440        0.6060             nan     0.1000   -0.0004
##    460        0.5926             nan     0.1000   -0.0002
##    480        0.5832             nan     0.1000   -0.0002
##    500        0.5742             nan     0.1000   -0.0004
## 
## - Fold08: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3358             nan     0.1000    0.0141
##      2        1.3115             nan     0.1000    0.0118
##      3        1.2904             nan     0.1000    0.0107
##      4        1.2714             nan     0.1000    0.0083
##      5        1.2558             nan     0.1000    0.0076
##      6        1.2415             nan     0.1000    0.0062
##      7        1.2293             nan     0.1000    0.0050
##      8        1.2174             nan     0.1000    0.0052
##      9        1.2072             nan     0.1000    0.0041
##     10        1.1939             nan     0.1000    0.0059
##     20        1.1103             nan     0.1000    0.0008
##     40        1.0015             nan     0.1000    0.0019
##     60        0.9395             nan     0.1000    0.0014
##     80        0.8887             nan     0.1000   -0.0002
##    100        0.8444             nan     0.1000    0.0006
##    120        0.8115             nan     0.1000    0.0006
##    140        0.7798             nan     0.1000    0.0007
##    160        0.7536             nan     0.1000    0.0003
##    180        0.7299             nan     0.1000   -0.0000
##    200        0.7063             nan     0.1000   -0.0000
##    220        0.6825             nan     0.1000   -0.0001
##    240        0.6631             nan     0.1000   -0.0002
##    260        0.6400             nan     0.1000    0.0003
##    280        0.6221             nan     0.1000   -0.0003
##    300        0.6073             nan     0.1000   -0.0005
##    320        0.5891             nan     0.1000   -0.0005
##    340        0.5748             nan     0.1000   -0.0007
##    360        0.5608             nan     0.1000   -0.0005
##    380        0.5476             nan     0.1000   -0.0005
##    400        0.5333             nan     0.1000   -0.0005
##    420        0.5207             nan     0.1000   -0.0001
##    440        0.5087             nan     0.1000   -0.0005
##    460        0.4963             nan     0.1000   -0.0007
##    480        0.4844             nan     0.1000   -0.0002
##    500        0.4733             nan     0.1000   -0.0010
## 
## - Fold08: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3312             nan     0.1000    0.0152
##      2        1.3015             nan     0.1000    0.0127
##      3        1.2795             nan     0.1000    0.0095
##      4        1.2599             nan     0.1000    0.0085
##      5        1.2421             nan     0.1000    0.0074
##      6        1.2267             nan     0.1000    0.0057
##      7        1.2089             nan     0.1000    0.0083
##      8        1.1936             nan     0.1000    0.0068
##      9        1.1802             nan     0.1000    0.0062
##     10        1.1687             nan     0.1000    0.0048
##     20        1.0854             nan     0.1000    0.0025
##     40        0.9795             nan     0.1000    0.0002
##     60        0.8986             nan     0.1000    0.0004
##     80        0.8460             nan     0.1000    0.0004
##    100        0.7988             nan     0.1000    0.0012
##    120        0.7602             nan     0.1000    0.0004
##    140        0.7216             nan     0.1000   -0.0004
##    160        0.6896             nan     0.1000   -0.0002
##    180        0.6576             nan     0.1000    0.0006
##    200        0.6313             nan     0.1000   -0.0003
##    220        0.6090             nan     0.1000   -0.0005
##    240        0.5850             nan     0.1000   -0.0002
##    260        0.5644             nan     0.1000   -0.0003
##    280        0.5445             nan     0.1000    0.0002
##    300        0.5261             nan     0.1000   -0.0002
##    320        0.5094             nan     0.1000   -0.0001
##    340        0.4902             nan     0.1000   -0.0004
##    360        0.4754             nan     0.1000   -0.0003
##    380        0.4590             nan     0.1000   -0.0001
##    400        0.4443             nan     0.1000   -0.0001
##    420        0.4307             nan     0.1000   -0.0000
##    440        0.4170             nan     0.1000   -0.0004
##    460        0.4053             nan     0.1000   -0.0003
##    480        0.3924             nan     0.1000    0.0000
##    500        0.3813             nan     0.1000   -0.0002
## 
## - Fold08: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3322             nan     0.1000    0.0160
##      2        1.3068             nan     0.1000    0.0103
##      3        1.2836             nan     0.1000    0.0097
##      4        1.2615             nan     0.1000    0.0093
##      5        1.2442             nan     0.1000    0.0074
##      6        1.2284             nan     0.1000    0.0067
##      7        1.2134             nan     0.1000    0.0068
##      8        1.1971             nan     0.1000    0.0068
##      9        1.1815             nan     0.1000    0.0071
##     10        1.1688             nan     0.1000    0.0050
##     20        1.0801             nan     0.1000    0.0023
##     40        0.9479             nan     0.1000    0.0015
##     60        0.8657             nan     0.1000    0.0001
##     80        0.8073             nan     0.1000    0.0005
##    100        0.7556             nan     0.1000   -0.0000
##    120        0.7079             nan     0.1000   -0.0000
##    140        0.6670             nan     0.1000   -0.0002
##    160        0.6344             nan     0.1000   -0.0004
##    180        0.6051             nan     0.1000   -0.0002
##    200        0.5769             nan     0.1000   -0.0007
##    220        0.5544             nan     0.1000   -0.0004
##    240        0.5315             nan     0.1000   -0.0005
##    260        0.5088             nan     0.1000   -0.0003
##    280        0.4884             nan     0.1000    0.0001
##    300        0.4689             nan     0.1000   -0.0001
##    320        0.4527             nan     0.1000    0.0000
##    340        0.4366             nan     0.1000   -0.0003
##    360        0.4191             nan     0.1000   -0.0003
##    380        0.4046             nan     0.1000   -0.0004
##    400        0.3905             nan     0.1000   -0.0002
##    420        0.3759             nan     0.1000   -0.0002
##    440        0.3622             nan     0.1000   -0.0004
##    460        0.3488             nan     0.1000   -0.0002
##    480        0.3363             nan     0.1000   -0.0003
##    500        0.3255             nan     0.1000   -0.0002
## 
## - Fold08: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3294             nan     0.1000    0.0172
##      2        1.2979             nan     0.1000    0.0140
##      3        1.2693             nan     0.1000    0.0127
##      4        1.2496             nan     0.1000    0.0090
##      5        1.2280             nan     0.1000    0.0084
##      6        1.2082             nan     0.1000    0.0075
##      7        1.1899             nan     0.1000    0.0093
##      8        1.1724             nan     0.1000    0.0075
##      9        1.1595             nan     0.1000    0.0051
##     10        1.1467             nan     0.1000    0.0051
##     20        1.0396             nan     0.1000    0.0026
##     40        0.9012             nan     0.1000    0.0018
##     60        0.8181             nan     0.1000   -0.0005
##     80        0.7545             nan     0.1000   -0.0003
##    100        0.7025             nan     0.1000   -0.0000
##    120        0.6540             nan     0.1000   -0.0001
##    140        0.6165             nan     0.1000   -0.0001
##    160        0.5827             nan     0.1000    0.0001
##    180        0.5520             nan     0.1000   -0.0003
##    200        0.5213             nan     0.1000   -0.0002
##    220        0.4928             nan     0.1000   -0.0007
##    240        0.4666             nan     0.1000   -0.0003
##    260        0.4469             nan     0.1000   -0.0005
##    280        0.4257             nan     0.1000   -0.0005
##    300        0.4045             nan     0.1000   -0.0004
##    320        0.3871             nan     0.1000   -0.0004
##    340        0.3682             nan     0.1000   -0.0001
##    360        0.3514             nan     0.1000   -0.0005
##    380        0.3355             nan     0.1000   -0.0002
##    400        0.3211             nan     0.1000   -0.0003
##    420        0.3071             nan     0.1000   -0.0002
##    440        0.2944             nan     0.1000   -0.0001
##    460        0.2815             nan     0.1000   -0.0003
##    480        0.2687             nan     0.1000   -0.0002
##    500        0.2576             nan     0.1000    0.0001
## 
## - Fold08: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3295             nan     0.1000    0.0169
##      2        1.2969             nan     0.1000    0.0142
##      3        1.2679             nan     0.1000    0.0139
##      4        1.2438             nan     0.1000    0.0097
##      5        1.2226             nan     0.1000    0.0090
##      6        1.2063             nan     0.1000    0.0070
##      7        1.1878             nan     0.1000    0.0075
##      8        1.1714             nan     0.1000    0.0057
##      9        1.1567             nan     0.1000    0.0061
##     10        1.1417             nan     0.1000    0.0059
##     20        1.0353             nan     0.1000    0.0016
##     40        0.8992             nan     0.1000    0.0018
##     60        0.8123             nan     0.1000    0.0005
##     80        0.7392             nan     0.1000   -0.0005
##    100        0.6842             nan     0.1000   -0.0000
##    120        0.6346             nan     0.1000    0.0008
##    140        0.5954             nan     0.1000   -0.0002
##    160        0.5568             nan     0.1000   -0.0004
##    180        0.5265             nan     0.1000   -0.0001
##    200        0.4963             nan     0.1000   -0.0002
##    220        0.4697             nan     0.1000    0.0002
##    240        0.4421             nan     0.1000   -0.0003
##    260        0.4173             nan     0.1000   -0.0001
##    280        0.3960             nan     0.1000   -0.0001
##    300        0.3736             nan     0.1000    0.0001
##    320        0.3549             nan     0.1000   -0.0003
##    340        0.3376             nan     0.1000   -0.0002
##    360        0.3202             nan     0.1000   -0.0000
##    380        0.3047             nan     0.1000   -0.0001
##    400        0.2914             nan     0.1000   -0.0003
##    420        0.2776             nan     0.1000   -0.0003
##    440        0.2635             nan     0.1000   -0.0003
##    460        0.2520             nan     0.1000   -0.0004
##    480        0.2415             nan     0.1000   -0.0001
##    500        0.2292             nan     0.1000   -0.0002
## 
## - Fold08: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3264             nan     0.1000    0.0194
##      2        1.2956             nan     0.1000    0.0132
##      3        1.2675             nan     0.1000    0.0134
##      4        1.2389             nan     0.1000    0.0128
##      5        1.2183             nan     0.1000    0.0077
##      6        1.1988             nan     0.1000    0.0083
##      7        1.1786             nan     0.1000    0.0088
##      8        1.1601             nan     0.1000    0.0079
##      9        1.1433             nan     0.1000    0.0074
##     10        1.1291             nan     0.1000    0.0056
##     20        1.0156             nan     0.1000    0.0031
##     40        0.8824             nan     0.1000    0.0006
##     60        0.7844             nan     0.1000    0.0012
##     80        0.7079             nan     0.1000   -0.0001
##    100        0.6535             nan     0.1000    0.0000
##    120        0.6019             nan     0.1000    0.0002
##    140        0.5544             nan     0.1000   -0.0004
##    160        0.5188             nan     0.1000   -0.0006
##    180        0.4879             nan     0.1000   -0.0001
##    200        0.4548             nan     0.1000    0.0002
##    220        0.4279             nan     0.1000   -0.0001
##    240        0.4030             nan     0.1000   -0.0001
##    260        0.3791             nan     0.1000   -0.0007
##    280        0.3571             nan     0.1000   -0.0004
##    300        0.3361             nan     0.1000   -0.0001
##    320        0.3171             nan     0.1000   -0.0002
##    340        0.3003             nan     0.1000   -0.0005
##    360        0.2858             nan     0.1000   -0.0006
##    380        0.2713             nan     0.1000   -0.0002
##    400        0.2564             nan     0.1000   -0.0005
##    420        0.2425             nan     0.1000   -0.0002
##    440        0.2298             nan     0.1000   -0.0002
##    460        0.2165             nan     0.1000   -0.0003
##    480        0.2055             nan     0.1000   -0.0003
##    500        0.1947             nan     0.1000   -0.0000
## 
## - Fold08: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold08: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3286             nan     0.1000    0.0171
##      2        1.2945             nan     0.1000    0.0155
##      3        1.2636             nan     0.1000    0.0143
##      4        1.2383             nan     0.1000    0.0108
##      5        1.2141             nan     0.1000    0.0096
##      6        1.1906             nan     0.1000    0.0090
##      7        1.1706             nan     0.1000    0.0066
##      8        1.1494             nan     0.1000    0.0081
##      9        1.1282             nan     0.1000    0.0088
##     10        1.1102             nan     0.1000    0.0063
##     20        0.9967             nan     0.1000    0.0018
##     40        0.8419             nan     0.1000    0.0030
##     60        0.7380             nan     0.1000   -0.0002
##     80        0.6617             nan     0.1000    0.0004
##    100        0.6024             nan     0.1000    0.0000
##    120        0.5511             nan     0.1000   -0.0003
##    140        0.5088             nan     0.1000    0.0001
##    160        0.4682             nan     0.1000   -0.0005
##    180        0.4323             nan     0.1000   -0.0007
##    200        0.4018             nan     0.1000   -0.0004
##    220        0.3710             nan     0.1000   -0.0004
##    240        0.3463             nan     0.1000   -0.0005
##    260        0.3238             nan     0.1000   -0.0002
##    280        0.3014             nan     0.1000   -0.0002
##    300        0.2823             nan     0.1000   -0.0003
##    320        0.2653             nan     0.1000   -0.0003
##    340        0.2492             nan     0.1000   -0.0004
##    360        0.2348             nan     0.1000   -0.0001
##    380        0.2209             nan     0.1000   -0.0001
##    400        0.2077             nan     0.1000   -0.0003
##    420        0.1956             nan     0.1000   -0.0001
##    440        0.1850             nan     0.1000   -0.0003
##    460        0.1746             nan     0.1000   -0.0002
##    480        0.1642             nan     0.1000   -0.0003
##    500        0.1539             nan     0.1000   -0.0002
## 
## - Fold08: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3473             nan     0.1000    0.0083
##      2        1.3326             nan     0.1000    0.0062
##      3        1.3181             nan     0.1000    0.0068
##      4        1.3061             nan     0.1000    0.0055
##      5        1.2970             nan     0.1000    0.0042
##      6        1.2883             nan     0.1000    0.0037
##      7        1.2800             nan     0.1000    0.0040
##      8        1.2730             nan     0.1000    0.0028
##      9        1.2674             nan     0.1000    0.0020
##     10        1.2612             nan     0.1000    0.0026
##     20        1.2186             nan     0.1000    0.0013
##     40        1.1763             nan     0.1000    0.0002
##     60        1.1498             nan     0.1000    0.0005
##     80        1.1300             nan     0.1000    0.0001
##    100        1.1161             nan     0.1000   -0.0004
##    120        1.1030             nan     0.1000   -0.0002
##    140        1.0934             nan     0.1000   -0.0005
##    160        1.0843             nan     0.1000   -0.0002
##    180        1.0753             nan     0.1000    0.0000
##    200        1.0674             nan     0.1000   -0.0003
##    220        1.0587             nan     0.1000    0.0001
##    240        1.0511             nan     0.1000   -0.0002
##    260        1.0448             nan     0.1000   -0.0005
##    280        1.0379             nan     0.1000   -0.0002
##    300        1.0317             nan     0.1000   -0.0002
##    320        1.0258             nan     0.1000   -0.0002
##    340        1.0196             nan     0.1000   -0.0003
##    360        1.0148             nan     0.1000   -0.0003
##    380        1.0079             nan     0.1000   -0.0002
##    400        1.0034             nan     0.1000   -0.0002
##    420        0.9983             nan     0.1000   -0.0002
##    440        0.9939             nan     0.1000   -0.0003
##    460        0.9884             nan     0.1000   -0.0002
##    480        0.9833             nan     0.1000    0.0000
##    500        0.9785             nan     0.1000   -0.0000
## 
## - Fold09: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3399             nan     0.1000    0.0120
##      2        1.3211             nan     0.1000    0.0093
##      3        1.3045             nan     0.1000    0.0083
##      4        1.2915             nan     0.1000    0.0053
##      5        1.2791             nan     0.1000    0.0056
##      6        1.2686             nan     0.1000    0.0046
##      7        1.2584             nan     0.1000    0.0042
##      8        1.2509             nan     0.1000    0.0034
##      9        1.2422             nan     0.1000    0.0036
##     10        1.2344             nan     0.1000    0.0032
##     20        1.1713             nan     0.1000    0.0022
##     40        1.0947             nan     0.1000    0.0005
##     60        1.0413             nan     0.1000    0.0004
##     80        1.0042             nan     0.1000    0.0013
##    100        0.9752             nan     0.1000    0.0009
##    120        0.9435             nan     0.1000    0.0006
##    140        0.9156             nan     0.1000    0.0002
##    160        0.8977             nan     0.1000    0.0000
##    180        0.8814             nan     0.1000   -0.0000
##    200        0.8623             nan     0.1000   -0.0002
##    220        0.8468             nan     0.1000    0.0006
##    240        0.8332             nan     0.1000   -0.0002
##    260        0.8177             nan     0.1000    0.0001
##    280        0.8059             nan     0.1000   -0.0005
##    300        0.7934             nan     0.1000   -0.0004
##    320        0.7794             nan     0.1000   -0.0004
##    340        0.7660             nan     0.1000   -0.0002
##    360        0.7549             nan     0.1000    0.0004
##    380        0.7428             nan     0.1000   -0.0004
##    400        0.7326             nan     0.1000   -0.0004
##    420        0.7241             nan     0.1000   -0.0004
##    440        0.7158             nan     0.1000   -0.0003
##    460        0.7072             nan     0.1000   -0.0004
##    480        0.6993             nan     0.1000   -0.0003
##    500        0.6907             nan     0.1000   -0.0001
## 
## - Fold09: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3388             nan     0.1000    0.0128
##      2        1.3118             nan     0.1000    0.0126
##      3        1.2939             nan     0.1000    0.0084
##      4        1.2770             nan     0.1000    0.0074
##      5        1.2633             nan     0.1000    0.0062
##      6        1.2483             nan     0.1000    0.0073
##      7        1.2360             nan     0.1000    0.0053
##      8        1.2232             nan     0.1000    0.0063
##      9        1.2130             nan     0.1000    0.0040
##     10        1.2026             nan     0.1000    0.0046
##     20        1.1252             nan     0.1000    0.0021
##     40        1.0314             nan     0.1000    0.0025
##     60        0.9765             nan     0.1000    0.0008
##     80        0.9306             nan     0.1000    0.0004
##    100        0.8942             nan     0.1000   -0.0003
##    120        0.8640             nan     0.1000   -0.0001
##    140        0.8386             nan     0.1000    0.0000
##    160        0.8156             nan     0.1000    0.0001
##    180        0.7945             nan     0.1000   -0.0005
##    200        0.7727             nan     0.1000   -0.0001
##    220        0.7524             nan     0.1000   -0.0004
##    240        0.7361             nan     0.1000   -0.0004
##    260        0.7200             nan     0.1000   -0.0009
##    280        0.7030             nan     0.1000   -0.0002
##    300        0.6893             nan     0.1000   -0.0006
##    320        0.6747             nan     0.1000   -0.0001
##    340        0.6596             nan     0.1000   -0.0004
##    360        0.6473             nan     0.1000   -0.0002
##    380        0.6357             nan     0.1000   -0.0003
##    400        0.6251             nan     0.1000   -0.0001
##    420        0.6135             nan     0.1000   -0.0002
##    440        0.6025             nan     0.1000   -0.0003
##    460        0.5936             nan     0.1000   -0.0001
##    480        0.5838             nan     0.1000   -0.0002
##    500        0.5741             nan     0.1000   -0.0003
## 
## - Fold09: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3311             nan     0.1000    0.0133
##      2        1.3050             nan     0.1000    0.0118
##      3        1.2812             nan     0.1000    0.0104
##      4        1.2638             nan     0.1000    0.0076
##      5        1.2482             nan     0.1000    0.0069
##      6        1.2321             nan     0.1000    0.0072
##      7        1.2199             nan     0.1000    0.0055
##      8        1.2042             nan     0.1000    0.0069
##      9        1.1916             nan     0.1000    0.0052
##     10        1.1808             nan     0.1000    0.0038
##     20        1.0891             nan     0.1000    0.0017
##     40        0.9842             nan     0.1000    0.0020
##     60        0.9084             nan     0.1000    0.0012
##     80        0.8555             nan     0.1000    0.0006
##    100        0.8203             nan     0.1000   -0.0001
##    120        0.7824             nan     0.1000    0.0007
##    140        0.7531             nan     0.1000   -0.0003
##    160        0.7212             nan     0.1000   -0.0004
##    180        0.6956             nan     0.1000   -0.0003
##    200        0.6739             nan     0.1000    0.0004
##    220        0.6545             nan     0.1000   -0.0005
##    240        0.6335             nan     0.1000   -0.0001
##    260        0.6155             nan     0.1000   -0.0002
##    280        0.5988             nan     0.1000   -0.0003
##    300        0.5820             nan     0.1000   -0.0002
##    320        0.5680             nan     0.1000   -0.0004
##    340        0.5526             nan     0.1000   -0.0003
##    360        0.5359             nan     0.1000   -0.0003
##    380        0.5212             nan     0.1000   -0.0001
##    400        0.5078             nan     0.1000   -0.0006
##    420        0.4964             nan     0.1000   -0.0005
##    440        0.4857             nan     0.1000   -0.0002
##    460        0.4720             nan     0.1000   -0.0003
##    480        0.4613             nan     0.1000   -0.0005
##    500        0.4499             nan     0.1000   -0.0004
## 
## - Fold09: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3345             nan     0.1000    0.0143
##      2        1.3042             nan     0.1000    0.0139
##      3        1.2791             nan     0.1000    0.0103
##      4        1.2582             nan     0.1000    0.0091
##      5        1.2392             nan     0.1000    0.0076
##      6        1.2213             nan     0.1000    0.0073
##      7        1.2045             nan     0.1000    0.0078
##      8        1.1913             nan     0.1000    0.0055
##      9        1.1784             nan     0.1000    0.0052
##     10        1.1653             nan     0.1000    0.0056
##     20        1.0714             nan     0.1000    0.0042
##     40        0.9441             nan     0.1000    0.0020
##     60        0.8694             nan     0.1000    0.0000
##     80        0.8165             nan     0.1000    0.0000
##    100        0.7685             nan     0.1000   -0.0001
##    120        0.7264             nan     0.1000   -0.0001
##    140        0.6904             nan     0.1000   -0.0005
##    160        0.6639             nan     0.1000    0.0004
##    180        0.6351             nan     0.1000   -0.0001
##    200        0.6099             nan     0.1000    0.0006
##    220        0.5873             nan     0.1000   -0.0005
##    240        0.5643             nan     0.1000    0.0001
##    260        0.5458             nan     0.1000   -0.0005
##    280        0.5287             nan     0.1000   -0.0004
##    300        0.5092             nan     0.1000    0.0004
##    320        0.4921             nan     0.1000   -0.0003
##    340        0.4744             nan     0.1000    0.0001
##    360        0.4574             nan     0.1000   -0.0003
##    380        0.4441             nan     0.1000   -0.0001
##    400        0.4291             nan     0.1000   -0.0001
##    420        0.4148             nan     0.1000   -0.0003
##    440        0.4031             nan     0.1000   -0.0004
##    460        0.3916             nan     0.1000   -0.0002
##    480        0.3803             nan     0.1000   -0.0003
##    500        0.3697             nan     0.1000   -0.0004
## 
## - Fold09: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3310             nan     0.1000    0.0155
##      2        1.3012             nan     0.1000    0.0132
##      3        1.2771             nan     0.1000    0.0107
##      4        1.2546             nan     0.1000    0.0102
##      5        1.2340             nan     0.1000    0.0085
##      6        1.2165             nan     0.1000    0.0075
##      7        1.1999             nan     0.1000    0.0075
##      8        1.1844             nan     0.1000    0.0067
##      9        1.1691             nan     0.1000    0.0069
##     10        1.1549             nan     0.1000    0.0062
##     20        1.0623             nan     0.1000    0.0029
##     40        0.9460             nan     0.1000    0.0010
##     60        0.8647             nan     0.1000   -0.0000
##     80        0.7998             nan     0.1000   -0.0001
##    100        0.7460             nan     0.1000   -0.0002
##    120        0.7076             nan     0.1000   -0.0003
##    140        0.6715             nan     0.1000   -0.0000
##    160        0.6356             nan     0.1000    0.0003
##    180        0.6025             nan     0.1000   -0.0004
##    200        0.5710             nan     0.1000    0.0002
##    220        0.5450             nan     0.1000   -0.0004
##    240        0.5241             nan     0.1000   -0.0008
##    260        0.5005             nan     0.1000   -0.0005
##    280        0.4794             nan     0.1000   -0.0002
##    300        0.4607             nan     0.1000   -0.0001
##    320        0.4447             nan     0.1000   -0.0003
##    340        0.4282             nan     0.1000   -0.0002
##    360        0.4116             nan     0.1000   -0.0000
##    380        0.3968             nan     0.1000   -0.0007
##    400        0.3835             nan     0.1000   -0.0004
##    420        0.3677             nan     0.1000   -0.0003
##    440        0.3543             nan     0.1000   -0.0002
##    460        0.3410             nan     0.1000   -0.0003
##    480        0.3267             nan     0.1000   -0.0002
##    500        0.3155             nan     0.1000   -0.0001
## 
## - Fold09: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3302             nan     0.1000    0.0163
##      2        1.2978             nan     0.1000    0.0142
##      3        1.2685             nan     0.1000    0.0132
##      4        1.2460             nan     0.1000    0.0098
##      5        1.2259             nan     0.1000    0.0076
##      6        1.2071             nan     0.1000    0.0073
##      7        1.1892             nan     0.1000    0.0075
##      8        1.1709             nan     0.1000    0.0078
##      9        1.1555             nan     0.1000    0.0057
##     10        1.1411             nan     0.1000    0.0057
##     20        1.0279             nan     0.1000    0.0029
##     40        0.8925             nan     0.1000    0.0058
##     60        0.8030             nan     0.1000    0.0010
##     80        0.7395             nan     0.1000   -0.0001
##    100        0.6891             nan     0.1000    0.0002
##    120        0.6409             nan     0.1000   -0.0002
##    140        0.6036             nan     0.1000   -0.0004
##    160        0.5701             nan     0.1000   -0.0002
##    180        0.5421             nan     0.1000   -0.0012
##    200        0.5134             nan     0.1000   -0.0003
##    220        0.4872             nan     0.1000    0.0001
##    240        0.4632             nan     0.1000    0.0001
##    260        0.4406             nan     0.1000    0.0002
##    280        0.4208             nan     0.1000   -0.0005
##    300        0.4012             nan     0.1000   -0.0004
##    320        0.3830             nan     0.1000   -0.0000
##    340        0.3660             nan     0.1000   -0.0003
##    360        0.3497             nan     0.1000   -0.0001
##    380        0.3337             nan     0.1000   -0.0005
##    400        0.3202             nan     0.1000   -0.0002
##    420        0.3077             nan     0.1000   -0.0001
##    440        0.2952             nan     0.1000   -0.0004
##    460        0.2826             nan     0.1000   -0.0001
##    480        0.2715             nan     0.1000   -0.0003
##    500        0.2604             nan     0.1000   -0.0001
## 
## - Fold09: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3295             nan     0.1000    0.0167
##      2        1.2970             nan     0.1000    0.0139
##      3        1.2681             nan     0.1000    0.0133
##      4        1.2402             nan     0.1000    0.0119
##      5        1.2152             nan     0.1000    0.0097
##      6        1.1942             nan     0.1000    0.0084
##      7        1.1763             nan     0.1000    0.0067
##      8        1.1620             nan     0.1000    0.0045
##      9        1.1477             nan     0.1000    0.0056
##     10        1.1331             nan     0.1000    0.0062
##     20        1.0191             nan     0.1000    0.0037
##     40        0.8899             nan     0.1000    0.0009
##     60        0.7997             nan     0.1000    0.0000
##     80        0.7285             nan     0.1000    0.0000
##    100        0.6714             nan     0.1000   -0.0004
##    120        0.6230             nan     0.1000   -0.0001
##    140        0.5823             nan     0.1000    0.0005
##    160        0.5453             nan     0.1000    0.0000
##    180        0.5117             nan     0.1000   -0.0001
##    200        0.4836             nan     0.1000   -0.0002
##    220        0.4565             nan     0.1000   -0.0006
##    240        0.4324             nan     0.1000   -0.0004
##    260        0.4079             nan     0.1000   -0.0002
##    280        0.3847             nan     0.1000   -0.0006
##    300        0.3650             nan     0.1000   -0.0003
##    320        0.3447             nan     0.1000   -0.0001
##    340        0.3262             nan     0.1000   -0.0004
##    360        0.3106             nan     0.1000   -0.0004
##    380        0.2965             nan     0.1000   -0.0004
##    400        0.2832             nan     0.1000   -0.0002
##    420        0.2689             nan     0.1000   -0.0002
##    440        0.2552             nan     0.1000   -0.0003
##    460        0.2445             nan     0.1000   -0.0002
##    480        0.2329             nan     0.1000   -0.0002
##    500        0.2217             nan     0.1000   -0.0002
## 
## - Fold09: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3249             nan     0.1000    0.0189
##      2        1.2897             nan     0.1000    0.0154
##      3        1.2580             nan     0.1000    0.0143
##      4        1.2317             nan     0.1000    0.0110
##      5        1.2063             nan     0.1000    0.0102
##      6        1.1855             nan     0.1000    0.0081
##      7        1.1640             nan     0.1000    0.0087
##      8        1.1447             nan     0.1000    0.0081
##      9        1.1298             nan     0.1000    0.0056
##     10        1.1132             nan     0.1000    0.0069
##     20        0.9968             nan     0.1000    0.0025
##     40        0.8557             nan     0.1000    0.0015
##     60        0.7634             nan     0.1000   -0.0002
##     80        0.7024             nan     0.1000   -0.0005
##    100        0.6439             nan     0.1000    0.0003
##    120        0.5933             nan     0.1000   -0.0005
##    140        0.5506             nan     0.1000   -0.0002
##    160        0.5101             nan     0.1000    0.0004
##    180        0.4776             nan     0.1000   -0.0001
##    200        0.4434             nan     0.1000    0.0004
##    220        0.4144             nan     0.1000   -0.0001
##    240        0.3911             nan     0.1000   -0.0003
##    260        0.3693             nan     0.1000   -0.0004
##    280        0.3494             nan     0.1000   -0.0001
##    300        0.3303             nan     0.1000   -0.0003
##    320        0.3115             nan     0.1000   -0.0001
##    340        0.2940             nan     0.1000   -0.0003
##    360        0.2783             nan     0.1000   -0.0003
##    380        0.2631             nan     0.1000   -0.0002
##    400        0.2503             nan     0.1000   -0.0003
##    420        0.2344             nan     0.1000   -0.0003
##    440        0.2226             nan     0.1000   -0.0003
##    460        0.2107             nan     0.1000   -0.0003
##    480        0.2000             nan     0.1000   -0.0001
##    500        0.1891             nan     0.1000   -0.0002
## 
## - Fold09: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold09: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3246             nan     0.1000    0.0192
##      2        1.2902             nan     0.1000    0.0145
##      3        1.2610             nan     0.1000    0.0115
##      4        1.2343             nan     0.1000    0.0112
##      5        1.2122             nan     0.1000    0.0085
##      6        1.1922             nan     0.1000    0.0080
##      7        1.1733             nan     0.1000    0.0071
##      8        1.1548             nan     0.1000    0.0054
##      9        1.1381             nan     0.1000    0.0068
##     10        1.1177             nan     0.1000    0.0089
##     20        0.9939             nan     0.1000    0.0052
##     40        0.8580             nan     0.1000    0.0007
##     60        0.7606             nan     0.1000    0.0013
##     80        0.6933             nan     0.1000    0.0002
##    100        0.6311             nan     0.1000   -0.0001
##    120        0.5813             nan     0.1000   -0.0006
##    140        0.5384             nan     0.1000   -0.0002
##    160        0.5001             nan     0.1000   -0.0009
##    180        0.4681             nan     0.1000   -0.0003
##    200        0.4375             nan     0.1000   -0.0008
##    220        0.4088             nan     0.1000   -0.0001
##    240        0.3831             nan     0.1000   -0.0007
##    260        0.3598             nan     0.1000    0.0000
##    280        0.3377             nan     0.1000   -0.0001
##    300        0.3186             nan     0.1000   -0.0004
##    320        0.2991             nan     0.1000   -0.0001
##    340        0.2803             nan     0.1000   -0.0004
##    360        0.2627             nan     0.1000   -0.0001
##    380        0.2474             nan     0.1000   -0.0003
##    400        0.2336             nan     0.1000   -0.0002
##    420        0.2198             nan     0.1000   -0.0002
##    440        0.2069             nan     0.1000   -0.0004
##    460        0.1965             nan     0.1000   -0.0003
##    480        0.1861             nan     0.1000   -0.0002
##    500        0.1761             nan     0.1000   -0.0002
## 
## - Fold09: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3484             nan     0.1000    0.0088
##      2        1.3334             nan     0.1000    0.0076
##      3        1.3193             nan     0.1000    0.0066
##      4        1.3079             nan     0.1000    0.0052
##      5        1.2981             nan     0.1000    0.0043
##      6        1.2880             nan     0.1000    0.0043
##      7        1.2803             nan     0.1000    0.0037
##      8        1.2703             nan     0.1000    0.0045
##      9        1.2631             nan     0.1000    0.0035
##     10        1.2563             nan     0.1000    0.0030
##     20        1.2169             nan     0.1000    0.0009
##     40        1.1728             nan     0.1000    0.0003
##     60        1.1428             nan     0.1000    0.0001
##     80        1.1222             nan     0.1000   -0.0000
##    100        1.1069             nan     0.1000    0.0001
##    120        1.0918             nan     0.1000   -0.0001
##    140        1.0805             nan     0.1000   -0.0002
##    160        1.0709             nan     0.1000   -0.0002
##    180        1.0608             nan     0.1000   -0.0003
##    200        1.0533             nan     0.1000   -0.0001
##    220        1.0469             nan     0.1000   -0.0001
##    240        1.0407             nan     0.1000   -0.0003
##    260        1.0331             nan     0.1000   -0.0002
##    280        1.0258             nan     0.1000   -0.0000
##    300        1.0200             nan     0.1000   -0.0001
##    320        1.0145             nan     0.1000   -0.0001
##    340        1.0081             nan     0.1000    0.0001
##    360        1.0034             nan     0.1000   -0.0005
##    380        0.9977             nan     0.1000   -0.0004
##    400        0.9932             nan     0.1000   -0.0001
##    420        0.9889             nan     0.1000   -0.0002
##    440        0.9836             nan     0.1000   -0.0002
##    460        0.9787             nan     0.1000   -0.0003
##    480        0.9740             nan     0.1000   -0.0003
##    500        0.9694             nan     0.1000   -0.0001
## 
## - Fold10: shrinkage=0.1, interaction.depth= 1, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3439             nan     0.1000    0.0105
##      2        1.3259             nan     0.1000    0.0087
##      3        1.3099             nan     0.1000    0.0070
##      4        1.2962             nan     0.1000    0.0057
##      5        1.2842             nan     0.1000    0.0054
##      6        1.2750             nan     0.1000    0.0041
##      7        1.2637             nan     0.1000    0.0047
##      8        1.2537             nan     0.1000    0.0045
##      9        1.2450             nan     0.1000    0.0036
##     10        1.2374             nan     0.1000    0.0029
##     20        1.1778             nan     0.1000    0.0013
##     40        1.1037             nan     0.1000    0.0009
##     60        1.0533             nan     0.1000    0.0002
##     80        1.0186             nan     0.1000   -0.0002
##    100        0.9811             nan     0.1000    0.0005
##    120        0.9524             nan     0.1000    0.0002
##    140        0.9314             nan     0.1000   -0.0003
##    160        0.9076             nan     0.1000    0.0005
##    180        0.8925             nan     0.1000   -0.0001
##    200        0.8744             nan     0.1000   -0.0001
##    220        0.8552             nan     0.1000   -0.0001
##    240        0.8410             nan     0.1000   -0.0003
##    260        0.8246             nan     0.1000   -0.0003
##    280        0.8114             nan     0.1000   -0.0003
##    300        0.7987             nan     0.1000    0.0001
##    320        0.7852             nan     0.1000   -0.0004
##    340        0.7758             nan     0.1000   -0.0002
##    360        0.7644             nan     0.1000   -0.0003
##    380        0.7541             nan     0.1000   -0.0002
##    400        0.7443             nan     0.1000   -0.0001
##    420        0.7351             nan     0.1000   -0.0003
##    440        0.7251             nan     0.1000   -0.0001
##    460        0.7154             nan     0.1000   -0.0003
##    480        0.7067             nan     0.1000   -0.0000
##    500        0.6993             nan     0.1000   -0.0002
## 
## - Fold10: shrinkage=0.1, interaction.depth= 2, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3427             nan     0.1000    0.0112
##      2        1.3221             nan     0.1000    0.0105
##      3        1.3027             nan     0.1000    0.0088
##      4        1.2838             nan     0.1000    0.0080
##      5        1.2703             nan     0.1000    0.0051
##      6        1.2549             nan     0.1000    0.0070
##      7        1.2417             nan     0.1000    0.0060
##      8        1.2294             nan     0.1000    0.0049
##      9        1.2198             nan     0.1000    0.0039
##     10        1.2087             nan     0.1000    0.0045
##     20        1.1420             nan     0.1000    0.0016
##     40        1.0581             nan     0.1000    0.0014
##     60        0.9970             nan     0.1000    0.0001
##     80        0.9561             nan     0.1000    0.0002
##    100        0.9131             nan     0.1000   -0.0003
##    120        0.8792             nan     0.1000    0.0004
##    140        0.8494             nan     0.1000    0.0003
##    160        0.8206             nan     0.1000   -0.0005
##    180        0.8004             nan     0.1000    0.0005
##    200        0.7838             nan     0.1000   -0.0007
##    220        0.7625             nan     0.1000   -0.0003
##    240        0.7446             nan     0.1000    0.0002
##    260        0.7284             nan     0.1000   -0.0006
##    280        0.7127             nan     0.1000   -0.0006
##    300        0.6970             nan     0.1000   -0.0005
##    320        0.6824             nan     0.1000   -0.0005
##    340        0.6710             nan     0.1000   -0.0004
##    360        0.6575             nan     0.1000   -0.0004
##    380        0.6459             nan     0.1000   -0.0004
##    400        0.6329             nan     0.1000   -0.0002
##    420        0.6205             nan     0.1000    0.0002
##    440        0.6085             nan     0.1000    0.0002
##    460        0.5989             nan     0.1000   -0.0002
##    480        0.5877             nan     0.1000   -0.0002
##    500        0.5773             nan     0.1000   -0.0003
## 
## - Fold10: shrinkage=0.1, interaction.depth= 3, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3371             nan     0.1000    0.0132
##      2        1.3128             nan     0.1000    0.0115
##      3        1.2913             nan     0.1000    0.0098
##      4        1.2738             nan     0.1000    0.0076
##      5        1.2588             nan     0.1000    0.0059
##      6        1.2437             nan     0.1000    0.0072
##      7        1.2308             nan     0.1000    0.0056
##      8        1.2180             nan     0.1000    0.0042
##      9        1.2046             nan     0.1000    0.0056
##     10        1.1937             nan     0.1000    0.0048
##     20        1.1077             nan     0.1000    0.0032
##     40        1.0113             nan     0.1000    0.0007
##     60        0.9396             nan     0.1000    0.0007
##     80        0.8894             nan     0.1000    0.0003
##    100        0.8452             nan     0.1000    0.0010
##    120        0.8077             nan     0.1000    0.0009
##    140        0.7781             nan     0.1000   -0.0000
##    160        0.7477             nan     0.1000    0.0000
##    180        0.7167             nan     0.1000    0.0004
##    200        0.6913             nan     0.1000   -0.0001
##    220        0.6690             nan     0.1000    0.0000
##    240        0.6505             nan     0.1000   -0.0002
##    260        0.6307             nan     0.1000   -0.0004
##    280        0.6118             nan     0.1000    0.0000
##    300        0.5945             nan     0.1000   -0.0001
##    320        0.5790             nan     0.1000   -0.0003
##    340        0.5662             nan     0.1000   -0.0001
##    360        0.5528             nan     0.1000   -0.0002
##    380        0.5398             nan     0.1000   -0.0002
##    400        0.5249             nan     0.1000    0.0003
##    420        0.5127             nan     0.1000   -0.0005
##    440        0.4989             nan     0.1000   -0.0005
##    460        0.4875             nan     0.1000   -0.0001
##    480        0.4759             nan     0.1000   -0.0003
##    500        0.4656             nan     0.1000   -0.0000
## 
## - Fold10: shrinkage=0.1, interaction.depth= 4, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3363             nan     0.1000    0.0142
##      2        1.3119             nan     0.1000    0.0111
##      3        1.2893             nan     0.1000    0.0093
##      4        1.2664             nan     0.1000    0.0100
##      5        1.2473             nan     0.1000    0.0081
##      6        1.2313             nan     0.1000    0.0065
##      7        1.2156             nan     0.1000    0.0067
##      8        1.2018             nan     0.1000    0.0053
##      9        1.1893             nan     0.1000    0.0055
##     10        1.1754             nan     0.1000    0.0051
##     20        1.0849             nan     0.1000    0.0024
##     40        0.9635             nan     0.1000    0.0012
##     60        0.8911             nan     0.1000   -0.0003
##     80        0.8362             nan     0.1000    0.0006
##    100        0.7839             nan     0.1000    0.0004
##    120        0.7421             nan     0.1000   -0.0002
##    140        0.7101             nan     0.1000    0.0002
##    160        0.6785             nan     0.1000    0.0006
##    180        0.6510             nan     0.1000   -0.0002
##    200        0.6226             nan     0.1000   -0.0005
##    220        0.5990             nan     0.1000   -0.0005
##    240        0.5774             nan     0.1000   -0.0001
##    260        0.5563             nan     0.1000   -0.0004
##    280        0.5369             nan     0.1000   -0.0002
##    300        0.5198             nan     0.1000   -0.0003
##    320        0.5045             nan     0.1000   -0.0005
##    340        0.4895             nan     0.1000   -0.0006
##    360        0.4756             nan     0.1000   -0.0003
##    380        0.4607             nan     0.1000   -0.0005
##    400        0.4469             nan     0.1000   -0.0006
##    420        0.4347             nan     0.1000   -0.0003
##    440        0.4202             nan     0.1000    0.0000
##    460        0.4056             nan     0.1000   -0.0005
##    480        0.3932             nan     0.1000   -0.0006
##    500        0.3827             nan     0.1000   -0.0003
## 
## - Fold10: shrinkage=0.1, interaction.depth= 5, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3313             nan     0.1000    0.0158
##      2        1.3022             nan     0.1000    0.0118
##      3        1.2767             nan     0.1000    0.0118
##      4        1.2525             nan     0.1000    0.0101
##      5        1.2342             nan     0.1000    0.0070
##      6        1.2168             nan     0.1000    0.0068
##      7        1.1999             nan     0.1000    0.0065
##      8        1.1859             nan     0.1000    0.0053
##      9        1.1702             nan     0.1000    0.0063
##     10        1.1586             nan     0.1000    0.0046
##     20        1.0585             nan     0.1000    0.0018
##     40        0.9435             nan     0.1000    0.0009
##     60        0.8608             nan     0.1000   -0.0002
##     80        0.8029             nan     0.1000   -0.0002
##    100        0.7554             nan     0.1000    0.0001
##    120        0.7137             nan     0.1000    0.0000
##    140        0.6739             nan     0.1000    0.0001
##    160        0.6404             nan     0.1000   -0.0001
##    180        0.6130             nan     0.1000   -0.0003
##    200        0.5819             nan     0.1000   -0.0001
##    220        0.5525             nan     0.1000   -0.0002
##    240        0.5306             nan     0.1000   -0.0005
##    260        0.5099             nan     0.1000    0.0001
##    280        0.4915             nan     0.1000   -0.0002
##    300        0.4693             nan     0.1000   -0.0003
##    320        0.4508             nan     0.1000   -0.0001
##    340        0.4344             nan     0.1000   -0.0001
##    360        0.4179             nan     0.1000   -0.0003
##    380        0.4040             nan     0.1000   -0.0002
##    400        0.3893             nan     0.1000   -0.0004
##    420        0.3750             nan     0.1000   -0.0001
##    440        0.3629             nan     0.1000   -0.0003
##    460        0.3504             nan     0.1000   -0.0003
##    480        0.3381             nan     0.1000   -0.0004
##    500        0.3249             nan     0.1000   -0.0003
## 
## - Fold10: shrinkage=0.1, interaction.depth= 6, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3318             nan     0.1000    0.0162
##      2        1.3037             nan     0.1000    0.0124
##      3        1.2761             nan     0.1000    0.0122
##      4        1.2539             nan     0.1000    0.0084
##      5        1.2373             nan     0.1000    0.0057
##      6        1.2206             nan     0.1000    0.0066
##      7        1.2051             nan     0.1000    0.0062
##      8        1.1893             nan     0.1000    0.0060
##      9        1.1756             nan     0.1000    0.0046
##     10        1.1631             nan     0.1000    0.0047
##     20        1.0597             nan     0.1000    0.0033
##     40        0.9335             nan     0.1000    0.0023
##     60        0.8533             nan     0.1000    0.0005
##     80        0.7835             nan     0.1000    0.0004
##    100        0.7272             nan     0.1000    0.0006
##    120        0.6827             nan     0.1000    0.0002
##    140        0.6408             nan     0.1000   -0.0002
##    160        0.6028             nan     0.1000   -0.0005
##    180        0.5716             nan     0.1000   -0.0001
##    200        0.5396             nan     0.1000   -0.0003
##    220        0.5134             nan     0.1000   -0.0003
##    240        0.4893             nan     0.1000   -0.0004
##    260        0.4650             nan     0.1000   -0.0002
##    280        0.4446             nan     0.1000   -0.0005
##    300        0.4245             nan     0.1000   -0.0005
##    320        0.4063             nan     0.1000   -0.0004
##    340        0.3881             nan     0.1000   -0.0002
##    360        0.3703             nan     0.1000   -0.0004
##    380        0.3531             nan     0.1000   -0.0005
##    400        0.3390             nan     0.1000    0.0000
##    420        0.3254             nan     0.1000   -0.0003
##    440        0.3110             nan     0.1000   -0.0000
##    460        0.2985             nan     0.1000   -0.0003
##    480        0.2863             nan     0.1000   -0.0003
##    500        0.2746             nan     0.1000   -0.0001
## 
## - Fold10: shrinkage=0.1, interaction.depth= 7, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3281             nan     0.1000    0.0182
##      2        1.2964             nan     0.1000    0.0150
##      3        1.2667             nan     0.1000    0.0130
##      4        1.2413             nan     0.1000    0.0120
##      5        1.2197             nan     0.1000    0.0082
##      6        1.1998             nan     0.1000    0.0072
##      7        1.1834             nan     0.1000    0.0059
##      8        1.1660             nan     0.1000    0.0067
##      9        1.1522             nan     0.1000    0.0039
##     10        1.1359             nan     0.1000    0.0070
##     20        1.0175             nan     0.1000    0.0033
##     40        0.8766             nan     0.1000    0.0004
##     60        0.7901             nan     0.1000    0.0002
##     80        0.7242             nan     0.1000    0.0000
##    100        0.6682             nan     0.1000    0.0003
##    120        0.6212             nan     0.1000   -0.0007
##    140        0.5768             nan     0.1000    0.0005
##    160        0.5408             nan     0.1000    0.0000
##    180        0.5102             nan     0.1000   -0.0004
##    200        0.4805             nan     0.1000   -0.0003
##    220        0.4528             nan     0.1000   -0.0003
##    240        0.4259             nan     0.1000   -0.0003
##    260        0.4033             nan     0.1000   -0.0002
##    280        0.3829             nan     0.1000   -0.0001
##    300        0.3636             nan     0.1000   -0.0003
##    320        0.3432             nan     0.1000   -0.0005
##    340        0.3255             nan     0.1000   -0.0005
##    360        0.3079             nan     0.1000    0.0001
##    380        0.2933             nan     0.1000   -0.0005
##    400        0.2778             nan     0.1000   -0.0002
##    420        0.2644             nan     0.1000   -0.0004
##    440        0.2516             nan     0.1000   -0.0001
##    460        0.2401             nan     0.1000   -0.0003
##    480        0.2294             nan     0.1000   -0.0003
##    500        0.2185             nan     0.1000   -0.0001
## 
## - Fold10: shrinkage=0.1, interaction.depth= 8, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3294             nan     0.1000    0.0170
##      2        1.2977             nan     0.1000    0.0132
##      3        1.2687             nan     0.1000    0.0133
##      4        1.2420             nan     0.1000    0.0110
##      5        1.2177             nan     0.1000    0.0108
##      6        1.1964             nan     0.1000    0.0085
##      7        1.1770             nan     0.1000    0.0075
##      8        1.1589             nan     0.1000    0.0079
##      9        1.1416             nan     0.1000    0.0064
##     10        1.1285             nan     0.1000    0.0043
##     20        1.0049             nan     0.1000    0.0047
##     40        0.8575             nan     0.1000    0.0007
##     60        0.7638             nan     0.1000    0.0002
##     80        0.6983             nan     0.1000   -0.0001
##    100        0.6433             nan     0.1000    0.0003
##    120        0.5916             nan     0.1000    0.0004
##    140        0.5518             nan     0.1000    0.0002
##    160        0.5130             nan     0.1000   -0.0006
##    180        0.4809             nan     0.1000   -0.0003
##    200        0.4465             nan     0.1000    0.0001
##    220        0.4204             nan     0.1000   -0.0001
##    240        0.3939             nan     0.1000   -0.0003
##    260        0.3683             nan     0.1000   -0.0004
##    280        0.3468             nan     0.1000   -0.0003
##    300        0.3261             nan     0.1000   -0.0002
##    320        0.3090             nan     0.1000   -0.0001
##    340        0.2928             nan     0.1000   -0.0001
##    360        0.2760             nan     0.1000   -0.0001
##    380        0.2600             nan     0.1000   -0.0002
##    400        0.2448             nan     0.1000   -0.0004
##    420        0.2313             nan     0.1000   -0.0003
##    440        0.2182             nan     0.1000   -0.0003
##    460        0.2068             nan     0.1000   -0.0002
##    480        0.1959             nan     0.1000   -0.0002
##    500        0.1854             nan     0.1000   -0.0003
## 
## - Fold10: shrinkage=0.1, interaction.depth= 9, n.minobsinnode=10, n.trees=500 
## + Fold10: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3281             nan     0.1000    0.0172
##      2        1.2938             nan     0.1000    0.0156
##      3        1.2655             nan     0.1000    0.0106
##      4        1.2416             nan     0.1000    0.0096
##      5        1.2174             nan     0.1000    0.0094
##      6        1.1989             nan     0.1000    0.0062
##      7        1.1802             nan     0.1000    0.0073
##      8        1.1616             nan     0.1000    0.0066
##      9        1.1444             nan     0.1000    0.0068
##     10        1.1289             nan     0.1000    0.0052
##     20        1.0104             nan     0.1000    0.0045
##     40        0.8552             nan     0.1000    0.0008
##     60        0.7547             nan     0.1000    0.0004
##     80        0.6794             nan     0.1000   -0.0007
##    100        0.6205             nan     0.1000    0.0001
##    120        0.5691             nan     0.1000   -0.0001
##    140        0.5248             nan     0.1000   -0.0001
##    160        0.4855             nan     0.1000   -0.0002
##    180        0.4520             nan     0.1000   -0.0002
##    200        0.4210             nan     0.1000   -0.0005
##    220        0.3914             nan     0.1000   -0.0003
##    240        0.3667             nan     0.1000   -0.0007
##    260        0.3432             nan     0.1000   -0.0002
##    280        0.3214             nan     0.1000   -0.0004
##    300        0.3021             nan     0.1000   -0.0001
##    320        0.2828             nan     0.1000   -0.0003
##    340        0.2651             nan     0.1000   -0.0002
##    360        0.2490             nan     0.1000   -0.0005
##    380        0.2333             nan     0.1000   -0.0002
##    400        0.2209             nan     0.1000   -0.0001
##    420        0.2088             nan     0.1000   -0.0003
##    440        0.1963             nan     0.1000   -0.0003
##    460        0.1865             nan     0.1000   -0.0003
##    480        0.1757             nan     0.1000   -0.0001
##    500        0.1659             nan     0.1000   -0.0002
## 
## - Fold10: shrinkage=0.1, interaction.depth=10, n.minobsinnode=10, n.trees=500 
## Aggregating results
## Selecting tuning parameters
## Fitting n.trees = 50, interaction.depth = 1, shrinkage = 0.1, n.minobsinnode = 10 on full training set
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.3522             nan     0.1000    0.0068
##      2        1.3398             nan     0.1000    0.0056
##      3        1.3302             nan     0.1000    0.0049
##      4        1.3198             nan     0.1000    0.0050
##      5        1.3098             nan     0.1000    0.0037
##      6        1.3026             nan     0.1000    0.0038
##      7        1.2954             nan     0.1000    0.0035
##      8        1.2901             nan     0.1000    0.0023
##      9        1.2831             nan     0.1000    0.0028
##     10        1.2765             nan     0.1000    0.0026
##     20        1.2360             nan     0.1000    0.0012
##     40        1.1939             nan     0.1000    0.0003
##     50        1.1788             nan     0.1000    0.0003
# Summary of model

gbmpredict <- predict(gbmodel,newdata = test2GB)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 9.14641 mins
#plot(model2)

plot(gbmodel, print.thres = 0.5, type="S")

caret::confusionMatrix(gbmpredict,test2GB$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 872  89
##         X1 206 104
##                                           
##                Accuracy : 0.7679          
##                  95% CI : (0.7437, 0.7909)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.2785          
##                                           
##  Mcnemar's Test P-Value : 1.44e-11        
##                                           
##             Sensitivity : 0.53886         
##             Specificity : 0.80891         
##          Pos Pred Value : 0.33548         
##          Neg Pred Value : 0.90739         
##               Precision : 0.33548         
##                  Recall : 0.53886         
##                      F1 : 0.41352         
##              Prevalence : 0.15185         
##          Detection Rate : 0.08183         
##    Detection Prevalence : 0.24390         
##       Balanced Accuracy : 0.67388         
##                                           
##        'Positive' Class : X1              
## 
gbmpredict1 <- predict(gbmodel,newdata = train2GB)

caret::confusionMatrix(gbmpredict1,train2GB$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 1981  211
##         X1  537  240
##                                          
##                Accuracy : 0.7481         
##                  95% CI : (0.732, 0.7636)
##     No Information Rate : 0.8481         
##     P-Value [Acc > NIR] : 1              
##                                          
##                   Kappa : 0.2459         
##                                          
##  Mcnemar's Test P-Value : <2e-16         
##                                          
##             Sensitivity : 0.53215        
##             Specificity : 0.78674        
##          Pos Pred Value : 0.30888        
##          Neg Pred Value : 0.90374        
##               Precision : 0.30888        
##                  Recall : 0.53215        
##                      F1 : 0.39088        
##              Prevalence : 0.15190        
##          Detection Rate : 0.08084        
##    Detection Prevalence : 0.26170        
##       Balanced Accuracy : 0.65944        
##                                          
##        'Positive' Class : X1             
## 
#evalm(gbmodel)

J48

set.seed(108)
sample2 <- createDataPartition(newdata2$TenYearCHD,p=0.7,list = FALSE)

train2j48 <- newdata2[sample2, ]
test2j48  <- newdata2[-sample2,]
#train2j48 <- train2C5[,-2]
#test2j48 <- test2C5[,-2]
levels(train2j48$TenYearCHD) <- make.names(levels(train2j48$TenYearCHD))
levels(test2j48$TenYearCHD) <- make.names(levels(test2j48$TenYearCHD))

repeats <- 3
numbers <- 10
tunel <- 10

x <- trainControl(method = "repeatedcv",
                 number = numbers,
                 repeats = repeats,
                 classProbs = TRUE,
                 summaryFunction = twoClassSummary,
                 sampling = "smote",
                 verboseIter = FALSE,
                 savePredictions = TRUE
                 )
starttime <- Sys.time()
#mtry <- sqrt(ncol(newdata2))
#tgrid <- expand.grid(maxdepth = 25)
J48model <- caret::train(TenYearCHD~., data = train2j48, method = "J48",
               #
               trControl = x,
               metric = "ROC",
               tuneLength = tunel)
               #tunegrid=tgrid)
               #ntree=5)
# Summary of model

J48predict <- predict(J48model,newdata = test2j48)

endtime <- Sys.time()

print(endtime-starttime)
## Time difference of 32.3574 mins
#plot(c5model, print.thres = 0.5, type="S")

imp <- caret::varImp(J48model,useModel=TRUE,scale=FALSE)

plot(imp)

plot(J48model$finalModel)

# plot(c5model$finalModel)
# text(c5model$finalModel)


#fancyRpartPlot(J48model$finalModel,palettes=c("Blues","Oranges"))


caret::confusionMatrix(J48predict,test2j48$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  X0  X1
##         X0 813 109
##         X1 265  84
##                                           
##                Accuracy : 0.7057          
##                  95% CI : (0.6798, 0.7307)
##     No Information Rate : 0.8482          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.1422          
##                                           
##  Mcnemar's Test P-Value : 1.103e-15       
##                                           
##             Sensitivity : 0.43523         
##             Specificity : 0.75417         
##          Pos Pred Value : 0.24069         
##          Neg Pred Value : 0.88178         
##               Precision : 0.24069         
##                  Recall : 0.43523         
##                      F1 : 0.30996         
##              Prevalence : 0.15185         
##          Detection Rate : 0.06609         
##    Detection Prevalence : 0.27459         
##       Balanced Accuracy : 0.59470         
##                                           
##        'Positive' Class : X1              
## 
J48predict1 <- predict(J48model,newdata = train2j48)

caret::confusionMatrix(J48predict1,train2j48$TenYearCHD, positive="X1",mode="everything")
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   X0   X1
##         X0 2025  134
##         X1  493  317
##                                           
##                Accuracy : 0.7888          
##                  95% CI : (0.7737, 0.8034)
##     No Information Rate : 0.8481          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.3822          
##                                           
##  Mcnemar's Test P-Value : <2e-16          
##                                           
##             Sensitivity : 0.7029          
##             Specificity : 0.8042          
##          Pos Pred Value : 0.3914          
##          Neg Pred Value : 0.9379          
##               Precision : 0.3914          
##                  Recall : 0.7029          
##                      F1 : 0.5028          
##              Prevalence : 0.1519          
##          Detection Rate : 0.1068          
##    Detection Prevalence : 0.2728          
##       Balanced Accuracy : 0.7535          
##                                           
##        'Positive' Class : X1              
## 
J485res <- evalm(J48model)
## ***MLeval: Machine Learning Model Evaluation in R***
## Input: caret train function object
## Averaging probs.
## Group 1 type: repeatedcv
## Observations: 2969
## Number of groups: 1
## Observations per group: 2969
## Positive: X1
## Negative: X0
## Group: Group 1
## Positive: 451
## Negative: 2518
## ***Performance Metrics***

## Group 1 Optimal Informedness = 0.289029409537362
## Group 1 AUC-ROC = 0.68